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NASA NTRS1994

Future ultra-speed tube-flight

Future long-link, ultra-speed, surface transport systems will require electromagnetically (EM) driven and restrained vehicles operating under reduced-atmosphere in very straight tubes. Such tube-flight trains will be safe, energy conservative, pollution-free, and in a protected environment. Hypersonic (and even hyperballistic) speeds are theoretically achievable. Ultimate system choices will represent tradeoffs between amoritized capital costs (ACC) and operating costs. For example, long coasting links might employ aerodynamic lift coupled with EM restraint and drag make-up. Optimized, combined EM lift, and thrust vectors could reduce energy costs but at increased ACC. (Repulsive levitation can produce lift-over-drag l/d ratios a decade greater than aerodynamic), Alternatively, vehicle-emanated, induced-mirror fields in a conducting (aluminum sheet) road bed could reduce ACC but at substantial energy costs. Ultra-speed tube flight will demand fast-acting, high-precision sensors and computerized magnetic shimming. This same control system can maintain a magnetic 'guide way' invariant in inertial space with inertial detectors imbedded in tube structures to sense and correct for earth tremors. Ultra-speed tube flight can complete with aircraft for transit time and can provide even greater passenger convenience by single-model connections with local subways and feeder lines. Although cargo transport generally will not need to be performed at ultra speeds, such speeds may well be desirable for high throughput to optimize channel costs. Thus, a large and expensive pipeline might be replaced with small EM-driven pallets at high speeds.

Salter, Robert M.↗

DOE OSTI2021

Fast All-sky Radiation Model for Solar applications (FARMS) [SWR-16-18]

The Fast All-sky Radiation Model for Solar applications (FARMS) is used to compute cloudy irradiance. Radiative transfer (RT) models simulating broadband solar radiation have been widely used by atmospheric scientists to model solar resources for various energy applications such as operational forecasting. Due to the complexity of solving the RT equation, the computation under cloudy conditions can be extremely time consuming though many approximations (e.g. two-stream approach and delta-M truncation scheme) have been utilized. Thus, a more efficient RT model is crucial for model developers as a new option for approximating solar radiation at the land surface with minimal loss of accuracy. We have developed a fast all-sky radiation model for solar applications (FARMS) using the simplified clear-sky RT model, REST2, and simulated cloud transmittances and reflectances from the Rapid Radiation Transfer Model (RRTM) with a sixteen-stream Discrete Ordinates Radiative Transfer (DISORT). Simulated lookup tables (LUTs) of cloud transmittances and reflectances were created by varying cloud optical thicknesses, cloud particle sizes, and solar zenith angles. Equations with optimized parameters were fitted to the cloud transmittances and reflectances to develop the model. Using this model the all-sky solar irradiance at the land surface can be computed rapidly by combining REST2 with the cloud transmittances and reflectances. This new RT model is more than 1000 times faster than those currently utilized in solar resource assessment and forecasting since it does not explicitly solve the RT equation for each individual cloud condition. Our results indicate the accuracy of the fast radiative transfer model is comparable to or better than two-stream approximation in term of computing cloud transmittance and solar radiation.

Xie, Yu↗

DOE OSTI2023

Fast All-sky Radiation Model for Solar applications (FARMS) 2023 [SWR-16-18]

Xie, Yu↗

DOE OSTI2021

Integration of a Wigner effect-based energy storage system with an advanced nuclear reactor

In this work, an innovative energy storage concept based on the purposeful creation of defects in crystalline material by neutron irradiation is presented. Lattice defects are generated when heavy particles collide with the atoms in a crystal structure, i.e., if the incoming particles have enough energy, recoil atoms are displaced from their initial lattice sites. Most of the displaced atoms will eventually combine with nearby vacancies, but some of them will come to rest in non-ideal locations. The energy held by displaced atoms is called Wigner energy. Lattice defects can migrate and form clusters, and the Wigner energy can be released from these groupings if sufficient activation energy is provided. In the nuclear industry, this effect is well-known since it represented an issue for graphite-moderated reactors. This work presents the conceptual design of an engineering system that exploits this physical process to store the energy of neutrons in advanced reactor concepts. In the first part of the paper, the theoretical performance of an energy storage system based on the Wigner effect is described. Given the lattice properties and the compatibility with the harsh reactor environment, graphite was selected as the candidate material for the irradiation targets. Both experimental data and molecular dynamics simulations confirmed that this system can achieve performance comparable with state-of-the-art batteries in terms of stored energy density. In the second part of the paper, the engineering challenges of this innovative technology and the proposed solutions are described. After defining the optimal irradiation conditions, the different steps of the operation of the proposed energy system (from energy storing to energy harvesting) were defined. Finally, the integration of this concept with advanced reactor designs, i.e., a Sodium-cooled Fast Reactor and a Molten Salt-cooled Reactor, was investigated and the corresponding performance was evaluated.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

A Reinforcement Learning Approach to Augment Conventional PID Control in Nuclear Power Plant Transient Operation

The ability of nuclear reactors to operate their power conversion cycles more flexibly will enhance their value to energy grids with variable pricing. Current nuclear control systems are typically classical controllers that are often based on proportional-integral-derivative (PID) control. This paper presents a method of augmenting the existing PID control for difficult transient operations in nuclear power plants using a reinforcement learning–derived feedforward signal applied in real time. The agents, which are trained on a test thermal load-following problem, are designed to improve steam generator outlet temperature control for a range of fast load-following scenarios covering ramp rates from 9%/min to 15%/min. Several reinforcement learning algorithms were initially investigated for the training of the feedforward agents with deep Q-learning (DQN) and proximal policy optimization (PPO) networks, which were found to be the most promising. The DQN controllers utilize discrete actions, giving them a better disturbance rejection at steady state but inconsistent response to initial temperature deviations. In contrast, PPO-trained agents, which take continuous actions except for a dead zone around zero, were shown to have the best combination of high disturbance rejection at steady state and good tracking of the desired temperature value. The ability of the PPO agent was also examined, with the average time of decision making found to be on the order of 1 ms. The fault properties of the controller under the loss of the reinforcement learning agent feedforward signal were also examined. The controller showed strong performance in situations of “no-signal” faults. but was less good at handling “stuck-at” faults, where the feedforward signal remains at a set value. In both cases, however, the PID was able to successfully maintain stability, eventually returning the system to a steady state. It is hoped that this work will allow for the proposed control architecture to be examined for more difficult control problems such that it may eventually be used to adapt existing nuclear plants for more aggressive load-following on grids of the future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Robust atom optics for Bragg atom interferometry

Multi-photon Bragg diffraction is a powerful method for fast, coherent momentum transfer of atom waves. However, laser noise, Doppler detunings, and cloud expansion limit its efficiency in large momentum transfer (LMT) pulse sequences. We present simulation studies of robust Bragg pulses developed through numerical quantum optimal control. Optimized pulse performance under noise and cloud inhomogeneities is analyzed and compared to analogous Gaussian and adiabatic rapid passage pulses in simulated LMT Mach–Zehnder interferometry sequences. The optimized pulses maintain robust population transfer and phase response over a broader range of noise, resulting in superior contrast in LMT sequences with thermal atom clouds and intensity inhomogeneities. Large optimized LMT sequences use lower pulse area than Gaussian pulses, making them less susceptible to spontaneous emission loss. The optimized sequences maintain over five times better contrast with tens of momentum separation and offer more improvement with greater LMT. Such pulses could allow operation of Bragg atom interferometers with unprecedented sensitivity, improved contrast, and hotter atom sources.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2020

Calorimetry with deep learning: particle simulation and reconstruction for collider physics

Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of single isolated particles produced in high-energy physics collisions. We train neural networks on single-particle shower data at the calorimeter-cell level, and show significant improvements for simulation and reconstruction when using these networks compared to methods which rely on currently-used state-of-the-art algorithms. We define two models: an end-to-end reconstruction network which performs simultaneous particle identification and energy regression of particles when given calorimeter shower data, and a generative network which can provide reasonable modeling of calorimeter showers for different particle types at specified angles and energies. We investigate the optimization of our models with hyperparameter scans. Furthermore, we demonstrate the applicability of the reconstruction model to shower inputs from other detector geometries, specifically ATLAS-like and CMS-like geometries. These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

hiposa v0.1.0

Fast construction of hierarchical N-dimensional experimental design. The software allows construction of sampling strategies that are self-avoiding, tunable in resolution in N dimensions. This code is a drop-in replacement for seeding optimization runs often performed using uniform random sampling. The hierarchical approach allows for a rapid, top down method for construction the most effective design that drives towards a global optimum.

Zwart, PetrusH [Lawrence Berkeley National Laborat↗

NASA NTRS1984

Integrated analysis of remote sensing products from basic geological surveys

Recent advances in remote sensing led to the development of several techniques to obtain image information. These techniques as effective tools in geological maping are analyzed. A strategy for optimizing the images in basic geological surveying is presented. It embraces as integrated analysis of spatial, spectral, and temporal data through photoptic (color additive viewer) and computer processing at different scales, allowing large areas survey in a fast, precise, and low cost manner.

Dasilvafagundesfilho, E.↗

NASA NTRS1987

Optimization of the computational load of a hypercube supercomputer onboard a mobile robot

A combinatorial optimization methodology is developed, which enables the efficient use of hypercube multiprocessors onboard mobile intelligent robots dedicated to time-critical missions. The methodology is implemented in terms of large-scale concurrent algorithms based either on fast simulated annealing, or on nonlinear asynchronous neural networks. In particular, analytic expressions are given for the effect of single-neuron perturbations on the systems' configuration energy. Compact neuromorphic data structures are used to model effects such as precedence constraints, processor idling times, and task-schedule overlaps. Results for a typical robot-dynamics benchmark are presented.

Barhen, Jacob↗

NASA NTRS2016

Performance Evaluation of the Approaches and Algorithms for Hamburg Airport Operations

The German Aerospace Center (DLR) and the National Aeronautics and Space Administration (NASA) have been independently developing and testing their own concepts and tools for airport surface traffic management. Although these concepts and tools have been tested individually for European and US airports, they have never been compared or analyzed side-by-side. This paper presents the collaborative research devoted to the evaluation and analysis of two different surface management concepts. Hamburg Airport was used as a common test bed airport for the study. First, two independent simulations using the same traffic scenario were conducted: one by the DLR team using the Controller Assistance for Departure Optimization (CADEO) and the Taxi Routing for Aircraft: Creation and Controlling (TRACC) in a real-time simulation environment, and one by the NASA team based on the Spot and Runway Departure Advisor (SARDA) in a fast-time simulation environment. A set of common performance metrics was defined. The simulation results showed that both approaches produced operational benefits in efficiency, such as reducing taxi times, while maintaining runway throughput. Both approaches generated the gate pushback schedule to meet the runway schedule, such that the runway utilization was maximized. The conflict-free taxi guidance by TRACC helped avoid taxi conflicts and reduced taxiing stops, but the taxi benefit needed be assessed together with runway throughput to analyze the overall performance objective.

air traffic management↗

NASA NTRS2016

Performance Evaluation of the Approaches and Algorithms using Hamburg Airport Operations

The German Aerospace Center (DLR) and the National Aeronautics and Space Administration (NASA) have been independently developing and testing their own concepts and tools for airport surface traffic management. Although these concepts and tools have been tested individually for European and US airports, they have never been compared or analyzed side-by-side. This paper presents the collaborative research devoted to the evaluation and analysis of two different surface management concepts. Hamburg Airport was used as a common test bed airport for the study. First, two independent simulations using the same traffic scenario were conducted: one by the DLR team using the Controller Assistance for Departure Optimization (CADEO) and the Taxi Routing for Aircraft58; Creation and Controlling (TRACC) in a real-time simulation environment, and one by the NASA team based on the Spot and Runway Departure Advisor (SARDA) in a fast-time simulation environment. A set of common performance metrics was defined. The simulation results showed that both approaches produced operational benefits in efficiency, such as reducing taxi times, while maintaining runway throughput. Both approaches generated the gate pushback schedule to meet the runway schedule, such that the runway utilization was maximized. The conflict-free taxi guidance by TRACC helped avoid taxi conflicts and reduced taxiing stops, but the taxi benefit needed be assessed together with runway throughput to analyze the overall performance objective.

Airport surface operations↗

NASA NTRS2016

Performance Evaluation of the Approaches and Algorithms Using Hamburg Airport Operations

The German Aerospace Center (DLR) and the National Aeronautics and Space Administration (NASA) have been independently developing and testing their own concepts and tools for airport surface traffic management. Although these concepts and tools have been tested individually for European and US airports, they have never been compared or analyzed side-by-side. This paper presents the collaborative research devoted to the evaluation and analysis of two different surface management concepts. Hamburg Airport was used as a common test bed airport for the study. First, two independent simulations using the same traffic scenario were conducted; one by the DLR team using the Controller Assistance for Departure Optimization (CADEO) and the Taxi Routing for Aircraft: Creation and Controlling (TRACC) in a real-time simulation environment, and one by the NASA team based on the Spot and Runway Departure Advisor (SARDA) in a fast-time simulation environment. A set of common performance metrics was defined. The simulation results showed that both approaches produced operational benefits in efficiency, such as reducing taxi times, while maintaining runway throughput. Both approaches generated the gate pushback schedule to meet the runway schedule, such that the runway utilization was maximized. The conflict-free taxi guidance by TRACC helped avoid taxi conflicts and reduced taxiing stops, but the taxi benefit needed be assessed together with runway throughput to analyze the overall performance objective.

nasa-dlr research collaboration↗

DOE OSTI2021

Data Analysis Approach for Large Data Volumes in a Connected Community

Recent advancements within smart neighborhoods where utilities are enabling automatic control of appliances such as heating, ventilation, and air conditioning (HVAC) and water heater (WH) systems are providing new opportunities to minimize energy costs through reduced peak load. This requires systematic collection, storage, management, and in-memory processing of large volumes of streaming data for fast performance. In this paper, we propose a multi-tier layered IoT software framework that enables effective descriptive and predictive data analysis for understanding live operation of the neighborhood, fault identification, and future opportunities for further optimization of load curves. We then demonstrate how we achieve live situational awareness of the connected neighborhood through a suite of visualization components. Finally, we discuss a few analytic dashboards that address questions such as peak load reductions obtained due to optimization, customer preference for automatic control of appliances (do they override the automatic control of HVAC?, etc.). 1 1 This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Chinthavali, Supriya↗

DOE OSTI2025

Sub-microsecond Transformers for Jet Tagging on FPGAs

We present the first sub-microsecond transformer implementation on an FPGA achieving competitive performance for state-of-the-art high-energy physics benchmarks. Transformers have shown exceptional performance on multiple tasks in modern machine learning applications, including jet tagging at the CERN Large Hadron Collider (LHC). However, their computational complexity prohibits use in real-time applications, such as the hardware trigger system of the collider experiments up until now. In this work, we demonstrate the first application of transformers for jet tagging on FPGAs, achieving $\mathcal{O}(100)$ nanosecond latency with superior performance compared to alternative baseline models. We leverage high-granularity quantization and distributed arithmetic optimization to fit the entire transformer model on a single FPGA, achieving the required throughput and latency. Furthermore, we add multi-head attention and linear attention support to hls4ml, making our work accessible to the broader fast machine learning community. This work advances the next-generation trigger systems for the High Luminosity LHC, enabling the use of transformers for real-time applications in high-energy physics and beyond.

Laatu, Lauri [Imperial Coll., London]↗

DOE OSTI2025

Traction Inverter Integrated On-Board DC Fast Charging through Partial Power Processing

This paper introduces an innovative on-board integrated DC charging approach through partial power processing (PPP) in a traction inverter system. The proposed system, incorporating a series-connected partial power dual-active-bridge (DAB) converter, efficiently regulates the DC link voltage to achieve an optimal bus voltage for traction operation across a wide speed and torque range. Leveraging partial and bidirectional power processing, the battery current during charging is precisely regulated without the need for external DC-DC charging equipment, ensuring seamless integration to a DC hub with different voltage levels. This advanced integration also endows the system with bidirectional power exchange capability to/from the external DC hub, i.e., vehicle-to-DC (V2DC).

DAB↗

DOE OSTI2025

Revealing the roles of the solid–electrolyte interphase in designing stable, fast-charging, low-temperature Li-ion batteries

Designing the solid–electrolyte interphase (SEI) is critical for stable, fast-charging, low-temperature Li-ion batteries. Fostering a “fluorinated interphase,” SEI enriched with LiF, has become a popular design strategy. Although LiF possesses low Li-ion conductivity, many studies have reported favorable battery performance with fluorinated SEIs. Such a contradiction suggests that optimizing SEI must extend beyond chemical composition design to consider spatial distributions of different chemical species. In this work, we demonstrate that the impact of a fluorinated SEI on battery performance should be evaluated on a case-by-case basis. Sufficiently passivating the anode surface without impeding Li-ion transport is key. We reveal that a fluorinated SEI containing excessive and dense LiF severely impedes Li-ion transport. In contrast, a fluorinated SEI with well-dispersed LiF (i.e., small LiF aggregates well mixed with other SEI components) is advantageous, presumably due to the enhanced Li-ion transport across heterointerfaces between LiF and other SEI components. An electrolyte, 1 M LiPF 6 in 2-methyl tetrahydrofuran (2MeTHF), yields a fluorinated SEI with dispersed LiF. This electrolyte allows anodes of graphite, μSi/graphite composite, and pure Si to all deliver a stable Coulombic efficiency of 99.9% and excellent rate capability at low temperatures. Pouch cells containing layered cathodes also demonstrate impressive cycling stability over 1,000 cycles and exceptional rate capability down to −20 °C. Through experiments and theoretical modeling, we have identified a balanced SEI-based approach that achieves stable, fast-charging, low-temperature Li-ion batteries.

25 ENERGY STORAGE↗

NASA NTRS2006

Automated Antenna Design with Evolutionary Algorithms

Current methods of designing and optimizing antennas by hand are time and labor intensive, and limit complexity. Evolutionary design techniques can overcome these limitations by searching the design space and automatically finding effective solutions. In recent years, evolutionary algorithms have shown great promise in finding practical solutions in large, poorly understood design spaces. In particular, spacecraft antenna design has proven tractable to evolutionary design techniques. Researchers have been investigating evolutionary antenna design and optimization since the early 1990s, and the field has grown in recent years as computer speed has increased and electromagnetic simulators have improved. Two requirements-compliant antennas, one for ST5 and another for TDRS-C, have been automatically designed by evolutionary algorithms. The ST5 antenna is slated to fly this year, and a TDRS-C phased array element has been fabricated and tested. Such automated evolutionary design is enabled by medium-to-high quality simulators and fast modern computers to evaluate computer-generated designs. Evolutionary algorithms automate cut-and-try engineering, substituting automated search though millions of potential designs for intelligent search by engineers through a much smaller number of designs. For evolutionary design, the engineer chooses the evolutionary technique, parameters and the basic form of the antenna, e.g., single wire for ST5 and crossed-element Yagi for TDRS-C. Evolutionary algorithms then search for optimal configurations in the space defined by the engineer. NASA's Space Technology 5 (ST5) mission will launch three small spacecraft to test innovative concepts and technologies. Advanced evolutionary algorithms were used to automatically design antennas for ST5. The combination of wide beamwidth for a circularly-polarized wave and wide impedance bandwidth made for a challenging antenna design problem. From past experience in designing wire antennas, we chose to constrain the evolutionary design to a monopole wire antenna. The results of the runs produced requirements-compliant antennas that were subsequently fabricated and tested. The evolved antenna has a number of advantages with regard to power consumption, fabrication time and complexity, and performance. Lower power requirements result from achieving high gain across a wider range of elevation angles, thus allowing a broader range of angles over which maximum data throughput can be achieved. Since the evolved antenna does not require a phasing circuit, less design and fabrication work is required. In terms of overall work, the evolved antenna required approximately three person-months to design and fabricate whereas the conventional antenna required about five. Furthermore, when the mission was modified and new orbital parameters selected, a redesign of the antenna to new requirements was required. The evolutionary system was rapidly modified and a new antenna evolved in a few weeks. The evolved antenna was shown to be compliant to the ST5 mission requirements. It has an unusual organic looking structure, one that expert antenna designers would not likely produce. This antenna has been tested, baselined and is scheduled to fly this year. In addition to the ST5 antenna, our laboratory has evolved an S-band phased array antenna element design that meets the requirements for NASA's TDRS-C communications satellite scheduled for launch early next decade. A combination of fairly broad bandwidth, high efficiency and circular polarization at high gain made for another challenging design problem. We chose to constrain the evolutionary design to a crossed-element Yagi antenna. The specification called for two types of elements, one for receive only and one for transmit/receive. We were able to evolve a single element design that meets both specifications thereby simplifying the antenna and reducing testing and integration costs. The highest performance antenna found using a getic algorithm and stochastic hill-climbing has been fabricated and tested. Laboratory results correspond well with simulation. Aerospace component design is an expensive and important step in space development. Evolutionary design can make a significant contribution wherever sufficiently fast, accurate and capable software simulators are available. We have demonstrated successful real-world design in the spacecraft antenna domain; and there is good reason to believe that these results could be replicated in other design spaces.

Hornby, Gregory S.↗