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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Single‐Step Deformation Processing of Ultrathin Lithium Foil and Strip

Abstract Next‐generation, high‐efficiency energy storage and conversion systems require development of lithium metal batteries. But the high cost of production and constraints on thickness of lithium (anode) foils continue to limit adoption for integration into battery cell architectures. Here, a novel lithium anode manufacturing solution is demonstrated – single‐step production of ultrathin gauge foil formats directly from solid ingot. Hybrid cutting‐based deformation processes, involving large plastic strains and strain rates, produce foil to sub‐10 µm thickness, with surface quality even superior to present Li anode processing routes. Energy analysis shows the single‐stage processing is ≈50% more efficient than conventional processing by extrusion‐rolling. Through in situ force measurements and high‐speed imaging of the cutting it also characterize – for the first time – the flow stress of Li to strain rates of 800 sec −1 , revealing a power‐law relationship. The results present a paradigm shift in manufacturing and integration of solid lithium anodes for energy applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells

Abstract Simultaneously optimizing the processing parameters of functional thin films remains a challenge. The design and utilization of a fully automated platform called SPINBOT is presented for the engineering of solution‐processed functional thin films. The SPINBOT is capable of performing experiments with high sampling variability through the unsupervised processing of hundreds of substrates with exceptional experimental control. Through the iterative optimization process enabled by the Bayesian optimization (BO) algorithm, the SPINBOT explores an intricate parameter space, continuously improving the quality and reproducibility of the produced thin films. This machine learning (ML)‐guided reliable SPINBOT platform enables the acceleration of the optimization process of perovskite solar cells via a simple photoluminescence characterization of films. As a result, this study arrives at an optimal film that, when processed into a solar cell in an ambient atmosphere, immediately yields a champion power conversion efficiency (PCE) of 21.6% with satisfactory performance reproducibility. The unsealed devices retain 90% of their initial efficiency after 1100 h of continuous operation at 60–65 °C under metal‐halide lamps. It is anticipated that the integration of robotic platforms with the intelligent algorithm will facilitate the widespread adoption of effective autonomous experimentation to address the evolving needs and constraints within the materials science research community.

14 SOLAR ENERGY↗
DOE OSTI2023

Crack detection in fuel cell electrodes using a spatial filtering technique for overcoming noisy backgrounds

Image processing is a powerful tool that allows for rapid and automated data parsing in settings that occupy large variable spaces and require large data sets. Feature detection on difficultly discerned backgrounds is a subset of image processing that facilitates the extraction of quantitative metrics from otherwise subjective data. Crack detection and quantification is an important capability in polymer electrolyte membrane fuel cell quality control, failure analysis, and optimization. This work presents a technique to perform crack detection and quantification which overcomes challenges faced by commonly used image segmentation techniques. We demonstrate the use of a geometrically filtered noise‐level detection technique to select a binary threshold value from which we then quantify how cracked a sample is. Furthermore, we demonstrate the accuracy of our technique using programmatically generated test images of known crack amounts and their performance on real‐world fuel cell catalyst layer samples.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Direct measurement of microwave loss in Nb films for superconducting qubits

Niobium films are a key component in modern two-dimensional superconducting qubits, yet their contribution to the total qubit decay rate is not fully understood. The presence of different layers of materials and interfaces makes it difficult to identify the dominant loss channels in present two-dimensional qubit designs. In this paper, we present the study that directly correlates measurements of RF losses in such films to material parameters by investigating a high-power impulse magnetron sputtered (HiPIMS) film atop a three-dimensional niobium superconducting radio frequency (SRF) resonator. By using a 3D SRF structure, we are able to isolate the niobium film loss from other contributions. Our findings indicate that microwave dissipation in the HiPIMS-prepared niobium films, within the quantum regime, resembles that of record-high intrinsic quality factor of bulk niobium SRF cavities, with lifetimes extending into seconds. Microstructure and impurity level of the niobium film do not significantly affect the losses. These results set the scale of microwave losses in niobium films and show that niobium losses do not dominate the observed coherence times in present two-dimensional superconducting qubit designs, instead highlighting the dominant role of the dielectric oxide in limiting the performance. We can also set a bound for when niobium film losses will become a limitation for qubit lifetimes.

Abdisatarov, B.↗
DOE OSTI2026

Towards Generalizable and Efficient Circuit Topology Design: A Graph-Transformer-based Surrogate Model with Curriculum Learning

Unlike circuit parameter and sizing optimizations, the automated design of analog circuit topologies poses significant challenges for learning-based approaches. One challenge arises from the combinatorial growth of the topology space with circuit size, which limits the topology optimization efficiency. Moreover, traditional circuit evaluation methods are time-consuming, while the presence of data discontinuity in the topology space makes the accurate prediction of circuit performance exceptionally difficult for unseen topologies. To tackle these challenges, we design a novel Graph-Transformer-based Network (GTN) as the surrogate model for circuit evaluation, offering a substantial acceleration in the speed of circuit topology optimization without sacrificing performance. Our GTN model architecture is designed to embed voltage changes in circuit loops and current flows in connected devices, enabling accurate performance predictions for circuits with unseen topologies. To address the cold start problem when scaling GTN to large-scale circuits, we further introduce a curriculum learning strategy that progressively trains GTN from small-scale to large-scale circuits. This approach enables the model to first learn fundamental physical principles from simpler topologies and gradually adapt to complex configurations, effectively bridging the circuit complexity gap and improving prediction accuracy. Taking the power converter circuit design as an experimental task, our GTN model significantly outperforms an analytical approach and baseline methods directly utilizing graph neural networks. Furthermore, GTN achieves less than 5% relative error and 196× speed-up compared with high-fidelity simulation. Notably, our GTN surrogate model empowers an automatic circuit design framework to discover circuits of comparable quality to those identified through high-fidelity simulation while reducing the time required by up to 98.2%. With curriculum learning, the enhanced GTN achieves a 51% improvement for performance prediction of large-scale circuits compared to the GTN model without this strategy. These advancements establish GTN as a scalable framework for automated analog circuit design across varying circuit complexity levels.

Lu, Haoshu [New Jersey Institute of Technology (NJ↗
DOE OSTI2022

Marine Renewable Energy Applications for Restorative Ocean Farming: Kelp

Kelp farming and kelp forest restoration have both been proposed as a solution to locally decrease the impacts of ocean acidification and eutrophication, often with co-benefits to other forms of aquaculture and mariculture. Compared to global markets, the kelp industry in the United States is still in its early phases, with the first commercial kelp farm founded in Casco Bay, Maine in 2010. Since then, interest and effort in kelp production has been increasing, with farms now present in Maine, New Hampshire, Connecticut, Rhode Island, Massachusetts, New York, Washington, and Alaska. Many research projects are underway in the United States to explore benefits of 3D ocean farming, tackle logistical problems of working in the ocean, autonomous farming, and explore viable end uses for kelp products. In seaweed farming, to remove the stored carbon or excess nutrients from the system, the biomass needs to be harvested at the optimal time to avoid the release of CO 2 that comes with decomposition. Timing of the harvest is also important for maximum crop yield, which can vary based on the final product. Additional monitoring needs can include a variety of water quality metrics, growth measurements, and visuals to ensure the health of the farm, comply with permits, support operations and maintenance functions. The variables measured may vary by desired end use of the product, location of farm, and operational design. Monitoring all of these parameters requires specialized devices that can be costly and challenging to maintain. Monitoring devices often face power and logistical constraints that could prevent kelp farmers from adopting these technologies or receiving accurate, efficient monitoring to assess ecosystem benefits and valuation. Marine energy has been identified as a possible power source for these devices. This project investigates the power needs for conducting kelp farm environmental monitoring compared with the available marine energy resource to evaluate if locally generated ocean energy could provide a solution to these monitoring challenges and benefit kelp farmers. This process was structured as follows: 1. Define what data is needed for farmers and their communities through desk research and interviews with end users. 2. Identify sensors and power requirements currently in use or available for commercial purchase. 3. Analyze current kelp and other mariculture farm locations for the potential marine energy resource. 4. Analyze farm designs and associated structures to make recommendations for marine energy design. 5. Quantify value that investment in sensors could provide in terms of carbon credit possibilities.

09 BIOMASS FUELS↗
DOE OSTI2024

Simplified, Infiltration-free Ceramic Matrix Composite Manufacturing

Manufacturing of ceramic matrix composites (CMCs) with carbon fiber and carbon matrix includes a time- and labor-intensive ceramic infiltration step that is responsible for more than half of the total manufacturing cost. To make CMCs cost-competitive in price-sensitive markets, like concentrated solar power, it is essential to develop a CMC manufacturing process that skips the ceramic infiltration process. In this work, we will show how a high-char-yield preceramic resin can eliminate the infiltration step while maintaining material density and evaluate the technoeconomic impact of our CMC manufacturing process. Here, we monitor both morphology and porosity to determine the quality of CMCs made with different preceramic resins and evaluate the impact of polymer infiltration and pyrolyzing cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High-Transmission Neutron Optical Devices Utilizing Micro-Machined Structures

Neutrons are a powerful probe in material science with unique penetrating abilities. A major challenge stems from the fact that neutron optical devices are limited to refractive indices on the order of n≈1±10−5. By exploiting advances in precision manufacturing, we designed and constructed micro-meter period triangular grating with a high-aspect ratio of 14.3. The manufacturing quality is demonstrated with white-light interferometric data and microscope imaging. Neutron-scattering experiment results are presented, showing agreement with refraction modelling. The capabilities of neutron Fresnel prisms and lenses based on this design are contrasted with existing neutron focusing techniques, and the path separation of a prism-based neutron interferometer is estimated.

Kapahi, Connor (ORCID:0000000160516859)↗
DOE OSTI2020

Tracking Radioactive Isotopes in HVAC and Application for Hot Cell Analyses - 20155

In recent years, the surge in the number of isotope production facilities under design has increased the need to analyze isotope migration in facility Heating, Ventilation, and Air Conditioning (HVAC) during normal operations and accident scenarios. These facilities are used to produce isotopes for medical, security, and industrial applications and as such are subject to license and regulatory requirements 10CFR20, 10CFR30, 10CFR50, and 10CFR70. GOTHIC, a general-purpose thermal-hydraulics software package, includes the ability to model isotopic tracers, radioactive decay and isotope migration as well as HEPA and charcoal filters for isotope retention. A GOTHIC model was developed by Zachry Nuclear Engineering (ZNE) to examine the effect of negative room pressure, HEPA filtration, and HVAC fluctuations on radiation areas, hot cells and gloveboxes. Radioactive tracers were used to simulate the concentration of spills within contaminated areas, track the migration of isotopes of interest, and determine the isotopic retention and buildup on facility HEPA filters. A variety of isotopes with concern to dose (e.g., Kr-85, Sr-90, I-131, etc.), including their decay and progeny, are included in the analysis. Negative pressures are maintained in the regions of interest by a representative central HVAC system equipped with a volumetric fan that exhausts to the environment after a series of isolation valves and HEPA filters. GOTHIC is an industry trusted tool for providing engineering solutions for a variety of applications, including fission product tracking, aerosol and particulate transport and ventilation assessments. The software provides an integrated analysis environment that includes a graphical user interface (GUI) for constructing analysis models, a numerical solver that includes parallel processing capabilities and a post-processor for evaluating simulation results. It solves the conservation equations for mass, momentum and energy for multicomponent, multi-phase flow in lumped parameter and multi-dimensional geometries (1, 2, or full 3D), including the effects of turbulence, diffusion and buoyancy. It has been developed and maintained under a Quality Assurance program in compliance with the requirements of 10CFR50 Appendix B and applicable portions of ASME NQA-1 since 1995. GOTHIC has been used for assessing both forced and natural convection conditions for a wide range of applications, including: - Tracking concentration of hazardous gases and chemicals for habitability and safety assessments - Determining ventilation and filtration requirements and optimizing location and arrangement of these systems - Room heat-up, including diverse and Flexible coping strategies for Extended Loss of AC Power (FLEX/ELAP) - Equipment Qualification (EQ) A distinctive feature of GOTHIC is the ability to track many different fields/substances in a simulation, including user defined tracer elements, in the liquid, vapor and droplet fields as well as surfaces and filters. This capability allows GOTHIC to model fission product transport and release or the removal of particulates or harmful toxins from exhaust gases using a spray scrubber or other types of filtration systems. GOTHIC also includes models for engineered equipment, such as fans, filters, charcoal filters, dryers/demisters, dampers, etc. The aerosols and other filtered material are removed or accumulated in these components. The range of aerosol and radiological applications that GOTHIC has been used for includes: - Source Term: Primary Coolant (Equilibrium) Activity; Non-Water Coolant Source/Leakage. - Conditions for Iodine Re-evolution: Sump/Suppression Pool Conditions and pH; - RWST Conditions and pH. - Isotope Removal Mechanisms: Containment Sprayed and Unsprayed Region Mixing; Charcoal Filter Heating due to Iodine decay. - Radionuclide Transport and Decay: Post-LOCA Release in containment; Transport between connected Compartments and vent systems; Groundwater transport of radionuclides. - Non-Newtonian Fluid modeling for sludge, waste tanks, etc. ADAMS ML071581053 (titled 'Best Practice Guidelines for the use of CFD in Nuclear Reactor Safety Applications') poses guidelines for applying single phase CFD codes in nuclear reactor safety problems and GOTHIC is listed as a 'tool for 3D flows' and 'dispersal and deposition of radionuclides.' The Nuclear Quality Assurance (NQA) pedigree of GOTHIC is an important aspect for applications in the nuclear industry. The fundamental tracer models (convective transport, molecular and turbulent diffusion, removal mechanisms, etc.) have been verified using analytical solutions and validated against applicable separate effects tests. Also, GOTHIC has been benchmarked to many integrated effects tests, including Phebus FP (Fission Product). GOTHIC gives good agreement for the buildup and decay of fission products in Phebus Test 3. The model developed by ZNE demonstrates GOTHIC's applicability and acceptability for use in analyzing the migration and retention of radioactive isotopes and their progeny in normal operation and accident scenario analyses for isotope production facilities, hot cells, and gloveboxes. The tracer activities calculated by GOTHIC can then be used in downstream radiation transport and shielding codes like RADTRAD-NAI{sup C}, MCNP{sup R}, and MicroShield{sup R} to determine on-site and of-site doses. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Solid State Transformer Architecture and Control Compensation for Common Mode Currents

A high-altitude electromagnetic pulse (HEMP) or similar geomagnetic disturbance (GMD) has the potential to impact the operation of large-scale electric power grids. By introducing low-frequency common-mode (CM) currents, these events can degrade the performance of critical system components, such as large power transformers by introducing CM currents which can lead to magnetic saturation of the transformer core. In this work, a solid-state transformer (SST) is developed to replace susceptible equipment and improve grid resiliency by safely absorbing these CM disturbances. This device will be referred to as a common-mode solid-state transformer (CM-SST). An SST architecture based on a four-legged AC/DC converter is developed. This architecture enables active control of CM signals without disturbing the AC voltages or the real and reactive power delivery capabilities. A system-level model of this architecture is created, and time-domain simulations are performed to evaluate the SST’s performance in response to simulated CM disturbances. A control strategy for mitigating CM current is also investigated. Hamiltonian surface shaping and power flow control (HSSPFC) is used to design a nonlinear controller for the SST’s output inverter. The objectives of the controller are to suppress CM-induced AC current offsets and regulate AC currents to desired setpoints. Nonlinear system analysis is applied to design and validate the controller. Two cases are tested: (a) the proposed four-leg inverter and (b) a standard three-leg inverter. The results show that the proposed controller rapidly mitigates CM disturbances while maintaining high-quality AC current waveforms in the four-leg configuration. Finally, the hardware performance of an SST prototype is evaluated. In particular, the ability of the SST to safely redirect and absorb CM currents is demonstrated, showing how it can protect neighboring conventional transformers in the system. The study confirms that appropriate control laws allow the SST to protect both itself and adjacent transformers during a HEMP or GMD event.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Error field predictability and consequences for ITER

Abstract ITER coil tolerances are re-evaluated using the modern understanding of coupling to least-stable plasma modes and an updated center-line-traced model of ITER’s coil windings. This reassessment finds the tolerances to be conservative through a statistical, linear study of n = 1 error fields (EFs) due to tilted, shifted misplacements and nominal windings of central solenoid and poloidal field coils within tolerance. We also show that a model-based correction scheme remains effective even when metrology quality is sub-optimal, and compare this to projected empirical correction schemes. We begin with an analysis of the necessity of error field correction (EFC) for daily operation in ITER using scalign laws for the EF penetration threshold. We then consider the predictability of EF dominant mode overlap across early planned ITER scenarios and, as measuring EFs in high power scenarios can pose risks to the device, the potential for extrapolation to the ITER Baseline Scenario (IBS). We find that carefully designing a scenario matching currents proportionally to those of the IBS is far more important than plasma shape or profiles in accurately measuring an optimal correction current set.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

SuperCDMS Event Reconstruction Using Convolutional Neural Networks [Thesis]

The SuperCDMS experiment uses cryogenic silicon and germanium detectors to search for dark matter candidates such as WIMPs (Weakly Interacting Massive Particles) streaming through the Earth. Collisions in the silicon and germanium crystals are expected to produce phonons whose thermal signatures can be measured. This thesis first describes the integration of a new Signal Distribution Unit (SDU) to the SuperCDMS data acquisition system, which allows for synchronization of multiple detectors and electronic/mechanical noise characterization via accelerometer, antenna, and AC phase measurements. From SuperCDMS detector data it is necessary to reconstruct the energies of the particle events. This thesis explores the use of Convolutional Neural Networks (CNNs) to perform this reconstruction and finds that, although they perform well, changing the noise model breaks the model and requires the neural network to be retrained. In order to mitigate this issue, a new CNN model is proposed which includes the noise Power Spectral Density (PSD) of the data as an additional input to the CNN. While it proves to be effective as a denoising algorithm, it still fails for data with a different noise model. However, including data from multiple PSDs in the neural network training sample allows it to handle data with different types of noise while still maintaining the quality of the reconstruction. Nevertheless, neural networks trained even on multiple PSDs do not robustly handle data taken with PSDs dissimilar to those in the training sample, suggesting that CNNs may need to be retrained whenever the noise environment changes in a significant way.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Power performance and loads characterization of laboratory-scale cross-flow rotors fabricated using additive manufacturing

Tidal energy conversion is a relatively new application for additive manufacturing (AM), where the focus has been on fabricating axial-flow turbine blades. AM techniques add material precisely where it is needed, creating more complex shapes with less waste. Cross-flow rotor geometry presents an opportunity for AM to improve rotor performance by fabricating features that cannot be created economically via conventional manufacturing. The challenges associated with using AM in cross-flow design include water resistance and degradation over time while retaining a level of quality equivalent to conventionally machined parts. In this work, AM materials were tested by environmentally conditioning samples in a seawater tank for 5 months, followed by performance and phase-resolved load testing of laboratory-scale rotors in a hydraulic flume. We found that while metals like titanium and Inconel have excellent performance in marine environments, achieving the desired geometry and performance is difficult. Thermoplastics degraded in seawater but were easier to form into desired geometries and could exceed the performance of an aluminum control rotor. Warping and surface finish were significant detractors from AM rotor performance. These results suggest that the primary benefits of using AM for cross-flow rotors is to quickly fabricate and test unconventional rotor geometries.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

On the formation of swelling and related flaws in laser powder bed fusion

Process monitoring in laser powder bed fusion additive manufacturing can provide insights into stochastic anomalies, melt pool and plume dynamics, and part quality. Swelling, a build anomaly where overbuilt material protrudes through the powder layer after recoating, is readily detectable in post-recoat visible light images of the powder bed. Here, this work identifies several of the underlying mechanisms driving swelling formation by analyzing the influence of processing parameters, laser scan paths, and build plate locations on the presence of swelling detected in situ. Swelling near the edge of the part and swelling in the internal region of the part are shown to correlate with different process conditions. Edge and internal swelling may be driven by different phenomena, with edge swelling predominately occurring on the edge of a part facing the laser module and correlated to clusters of near-surface voids (detected with X-ray computed tomography). A larger spot size, higher laser power, and lower scan velocity also increased the presence of edge swelling. Laser spot size and scan path influenced internal swelling, which occurred preferentially with a larger spot size and in regions with large melt pools, caused by localized heat accumulation due to non-optimal processing parameters or scan path strategies. For coupons processed with a slicer-defined maximum scan vector length, swelling seldom occurred at internal vector-stripe boundaries. These results provide a mechanistic understanding of how swelling can be linked to material flaws, insight into how some instances of swelling can be avoided, and evidence supporting the use of swelling as an in situ indicator for quality assurance and part qualification.

Anomaly↗
DOE OSTI2022

Sensor selection and tool wear prediction with data‐driven models for precision machining

Abstract Estimation of tool wear in precision machining is vital in the traditional subtractive machining industry to reduce processing cost, improve manufacturing efficiency and product quality. In this vein, fusion of time and frequency‐domain features of commonly sensed signals can provide an early indication of tool wear and improve its prediction accuracy for prognostics and health management. This paper presents a data‐driven methodology and a complete tool chain for the inference of precision machining tool wear from fused machine measurements, such as cutting force, power, audio and vibration signals, and quantify the usefulness of each measurement. Indicators of tool wear are extracted from time‐domain signal statistics, frequency‐domain analysis, and time‐frequency domain analysis. Correlation coefficients between the extracted features (indicators) and the tool wear are used to select the most informative features. Principal Component Analysis and Partial Least‐Squares are used to reduce the dimensionality of the feature space. Regression models, including linear regression, support vector regression, Decision tree regression, neural network regression and Gaussian process regression, are used to predict the tool wear using data from a Haas milling machine performing spiral boss face milling. The performance of the regression models based on subsets of sensors validates the preliminary estimates about the saliency of the sensors. The experimental results show that the proposed methods can predict the machine tool wear precisely, with readily available sensor measurements. Neural network and Gaussian process regression were able to achieve good estimates of tool wear at different machine operating conditions. The most informative signal in predicting tool wear was shown to be the vibration signal. Time‐frequency domain features were the most informative features among the combination of features of three domains. In addition, using partial least squares components extracted from the original features of signals led to higher prediction accuracy.

Han, Seulki↗
DOE OSTI2020

PV Field Reliability Status--Analysis of 100,000 Solar Systems

In this paper, we analyzed annual production data from 100 000 photovoltaic systems as well as comments relating to their performance and maintenance. Our analysis revealed that 80% to 90% of all systems performed within 10% of the predicted production or better. Also, 56% of the systems were still performing above P50 or the median at 5 years. However, a small but significant tail of about 7000 systems perform below P90 expectations. In general, residential systems have a lower rate of failure than utility or commercial systems. Despite higher rates of component failures, utility systems lose less power than residential or commercial systems. This outcome is likely due to closer monitoring and better operations and maintenance practices. Inverters are still the components that reportedly fail most often (4%-6%), but other failures such as unspecified repair and meters cause more production loss. Reported module failures are relatively rare (0.2%) and are within the range of historical values. Installation quality affects performance and safety as indicated by data showing connector, wiring, breaker, and fuse failures due to undersizing, electrical design, and improper connection. Lastly, early detection of degradation and proactive response resulted in less impact on production than reactive, unplanned repairs.

14 SOLAR ENERGY↗
DOE OSTI2021

Protein C-GeM: A Coarse-Grained Electron Model for Fast and Accurate Protein Electrostatics Prediction

The electrostatic potential (ESP) is a powerful property for understanding and predicting electrostatic charge distributions that drive interactions between molecules. In this study, we compare various charge partitioning schemes including fitted charges, density-based quantum mechanical (QM) partitioning schemes, charge equilibration methods, and our recently introduced coarse-grained electron model, C-GeM, to describe the ESP for protein systems. Furthermore, when benchmarked against high quality density functional theory calculations of the ESP for tripeptides and the crambin protein, we find that the C-GeM model is of comparable accuracy to ab initio charge partitioning methods, but with orders of magnitude improvement in computational efficiency since it does not require either the electron density or the electrostatic potential as input.

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

User-Centric Communication With Aerial Network for 6G: A Reinforcement Learning Approach

Meeting the diverse needs of user verticals requires innovative cellular architectures that can offer additional degrees of freedom to provide on-demand services. The terrestrial user-centric radio access network (UC-RAN) stands out as an excellent choice for this purpose. However, a drawback of UC-RAN is its tendency to prioritize high-priority verticals, often resulting in a subpar quality of experience for low-priority verticals. This issue is particularly exacerbated in hotspot areas. Here, to address this problem, we introduce an aerial network integrated with terrestrial UC-RAN to provide coverage to users which are not served by the terrestrial network. Furthermore, we analyze the impact of key configuration and optimization parameters (COPs), such as location, transmit power, altitude, and beamwidth of aerial base stations (ABSs) on system key performance indicators (KPIs), such as coverage, latency satisfaction, average spectral efficiency, and energy efficiency. We formulate a robust multiobjective function to maximize these KPIs without biasing toward any specific KPI(s). Finally, we propose a deep reinforcement learning optimization framework based on the state-of-the-art soft actor-critic algorithm to control ABS COPs and optimize system KPIs. Experimental evaluations demonstrate that the proposed optimization framework can converge to near-optimal solutions derived from the pseudo brute force in a few thousand epochs.

6G↗