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All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

International Space Power System Interoperability Standards (ISPSIS)

This International Space Power System Interoperability Standards(ISPSIS) is the result of collaborations within the International Space Station (ISS) and Gateway memberships to establish interoperable interfaces, terminology, and techniques, to facilitate collaborative endeavors of space exploration in cislunar and deep space environments. These standards are available for international and commercial partnerships. Standards that are established and internationally recognized have been selected where possible to enable a variety of providers. Increasing hardware commonality among providers while decreasing unique configurations has the potential to reduce the traditional barriers in space exploration: overall mass and volume required to execute a mission. Standardizing interfaces reduces the scope of the development effort. The information within this document represents a set of parameters, which if accommodated in the system architecture support greater efficiencies, promote cost savings, and increase the probability of mission success. These standards are not intended to specify system details needed for implementation nor do they dictate design features behind the interface; specific requirements will be defined in unique documents.

David James Sadey↗
NASA NTRS2024

Considerations in Range of Motion Testing for Characterizing Spacesuit Mobility Capabilities

Mobility is a critical aspect for spacesuits as it directly influences how astronauts can perform tasks safely and efficiently, while wearing the pressurized suit. Spacesuit motion is dissimilar to human movement and consists of unique movement patterns as result of pressurization and the complex mechanical joint configurations. Thus, it is important to quantify the variations in suited movement patterns and the resulting performance of the wearer for the evaluation of the spacesuit itself and hardware interfaces such as payloads and tools. Traditionally, an individual body joint is assessed for the segment-wise range of motion. For example, the knee is measured through an isolated maneuvering of the joint between the max-to-max positions (e.g., flexion/extension), and the outcome represents the mobility capabilities. These measurements are often conducted using optical motion capture system with respect to anatomical planes. However, there are several considerations for range of motion assessments that are unique to the spacesuit. The purpose of this paper is to describe and provide examples of these additional challenges in representing spacesuit mobility capabilities. Specifically, joint mobility and the variations in suit movement patterns were investigated and compared between functional tasks and isolated range of motion measurements. Motion capture data from the next-gen spacesuit design verification testing was evaluated to compare isolated range of motion tasks to functional tasks, such as one-knee kneeling and squatting. The trajectories from the upper and lower body joint centers with respect to the spacesuit hardware were also calculated. Additionally, the joint trajectories for various functional tasks are presented and compared against the isolated range of motion measurements. In general, range of motion differs between isolated and functional tasks, where functional tasks may even induce greater joint angle excursions. Additionally, there is a large variation in joint range of motion across functional tasks. Combined with several relevant factors (spacesuit fit, physical strength, simulation facility, etc.), spacesuit mobility characterization efforts will need to incorporate the specific contexts, such as task demands and movement mechanisms, when evaluating range of motion assessments.

Linh Q Vu↗
NASA NTRSDate not supplied

Considerations for Range of Motion Testing During Spacesuit Mobility Characterization

Mobility is a critical aspect for spacesuits as it directly influences how astronauts can perform tasks safely and efficiently, while wearing the pressurized suit. Spacesuit motion is dissimilar to human movement and consists of unique movement patterns as result of pressurization and the complex mechanical joint configurations. Thus, it is important to quantify the variations in suited movement patterns and the resulting performance of the wearer for the evaluation of the spacesuit itself and hardware interfaces such as payloads and tools. Traditionally, an individual body joint is assessed for the segment-wise range of motion. For example, the knee is measured through an isolated maneuvering of the joint between the max-to-max positions (e.g., flexion/extension), and the outcome represents the mobility capabilities. These measurements are often conducted using optical motion capture system with respect to anatomical planes. However, there are several considerations for range of motion assessments that are unique to the spacesuit. The purpose of this paper is to describe and provide examples of these additional challenges in representing spacesuit mobility capabilities. Specifically, joint mobility and the variations in suit movement patterns were investigated and compared between functional tasks and isolated range of motion measurements. Motion capture data from the next-gen spacesuit design verification testing was evaluated to compare isolated range of motion tasks to functional tasks, such as one-knee kneeling and squatting. The trajectories from the upper and lower body joint centers with respect to the spacesuit hardware were also calculated. Additionally, the joint trajectories for various functional tasks are presented and compared against the isolated range of motion measurements. In general, range of motion differs between isolated and functional tasks, where functional tasks may even induce greater joint angle excursions. Additionally, there is a large variation in joint range of motion across functional tasks. Combined with several relevant factors (spacesuit fit, physical strength, simulation facility, etc.), spacesuit mobility characterization efforts will need to incorporate the specific contexts, such as task demands and movement mechanisms, when evaluating range of motion assessments.

Linh Q Vu↗
NASA NTRSDate not supplied

High-Fidelity Modeling Approach for Coupling Thermal, Fluid, and Neutronic Analysis in an NTP Engine

Nuclear thermal propulsion (NTP), with its increased efficiency compared to traditional rocket engines, is a potentially enabling technology for space missions of higher complexity, such as delivering heavy machinery or crew to the moon or other planets. Thus, exploring modeling approaches that can effectively couple the thermal, fluid, and neutronic behaviors of NTP reactors is essential to mitigate cost and schedule challenges with hardware development. This paper details one such approach which utilizes Cardinal to unify reactor physics analysis performed using OpenMC, heat transfer analysis performed using the MOOSE framework, and thermal hydraulic analysis performed using the MOOSE Thermal Hydraulic Module. This approach is demonstrated with a proof of concept that focuses on a legacy reactor geometry from the historic NERVA program. This preliminary model found an overprediction in, when compared to the reference data, axial power peaking and the temperatures of both solids and fluids by roughly 20%. It is clear, however, that the model conserved a number of broad trends, and a few model refinements should reduce the observed error. This approach has definite merit and should enable the production of informative results for more modern NTP systems. This work is a first step in creating a transient reactor model that can be updated across a sequence of time steps based on continually fluctuating, time-dependent boundary conditions.

Nuclear Thermal Propulsion↗
DOE OSTI2022

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations. To make advancements in all the aforementioned scientific areas, many technical aspects need to be more thoroughly studied and deeply understood in parallel discrete event simulation (PDES). The unique dynamics inherent in a discrete event modeling approach, by their very nature, intersect and influence the entire stack of the computing system, including (a) the unique nature of the instruction sets exercised in PDES workloads without a predominance of high-precision floating point operations, (b) virtual time-constrained multi-threaded execution of many logical processes per processor, (c) extremely variable and difficult to predict network traffic characteristics, (d) interfaces and inter-dependencies with machine learning and artificial intelligence codes at higher software layers, and (e) highly challenging load balancing needs, especially in effectively accounting for accelerated/extremely heterogeneous computing in current and future high-performance computing systems. Efficient and accurate parallel execution of PDES workloads is also dominated by challenges in dealing with their asynchronous concurrency fundamentally present at the model level. Conservative synchronization, optimistic/speculative synchronization, and their hybrid schemes open new questions in fundamental computer science with respect to reversibility of computation and prediction (lookahead) of behaviors inherent within model codes. On the implementation front, there are relatively few scalable, general-purpose parallel discrete event simulators in the world, and even fewer have been studied on emerging hardware platforms. To enable scientific advances using PDES, the research needs in computer science must also be pursued and met in the intersection of the algorithmic and hardware-aware aspects of scalable PDES engines. This report is aimed at capturing a computer science-oriented view of this important area of research in PDES, presenting a sample of important applications with their inherent discrete event technology elements. Needs are outlined in core areas of parallel discrete event research as well as cross-cutting directions in computer science research that positively impact scientific advancements across several important application areas. A selection of priority research opportunities in advanced computing for PDES is identified to serve as reference for key research topics and their order of importance for scientific advancements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Efficient Implementation of Artificial Neural Networks for Sensor Data Analysis Based on a Genetic Algorithm

The reliability of many industrial processes depends on the sensor system. However, these sensors can be affected by noise, perturbations and failures. Hence, sensor monitoring and diagnosis are fundamental to guarantee the quality of an industrial process. Nowadays, artificial neural networks (ANN) are widely used in sensor signal processing and diagnosis. However, those ANNs usually require many artificial neurons, being difficult to implement in software and hardware due to their high computational costs. This paper presents an optimized implementation of artificial neurons in ANNs for sensor data analysis using a Genetic Algorithm (GA). The objective of GA is to find an adequate segmentation to reduce the activation function approximation error. One of the advantages of the proposed approach is that the cost function used in GA considers the effect of factors such as the ANN architecture or the number of bits used in arithmetic operations. The proposed ANN implementation technique aims to get the best possible approximation for a specific ANN architecture, making easier its implementation in software and hardware. Simulation and experimental results using FPGA (Field Programmable Gate Array) prove the advantages of the proposed approach for implementing sensor data analysis systems based on ANNs.

D estefani, André↗
DOE OSTI2023

Material Identification From Radiographs Without Energy Resolution

We propose a method for performing material identification from radiographs without energy-resolved measurements. Material identification has a wide variety of applications, including in biomedical imaging, nondestructive testing, and security. While existing techniques for radiographic material identification make use of dual energy sources, energy-resolving detectors, or additional (e.g., neutron) measurements, such setups are not always practical— requiring additional hardware and complicating imaging. We tackle material identification without energy resolution, allowing standard X-ray systems to provide material identification information without requiring additional hardware. Assuming a setting where the geometry of each object in the scene is known and the materials come from a known set of possible materials, we pose the problem as a combinatorial optimization with a loss function that accounts for the presence of scatter and an unknown gain and propose a branch and bound algorithm to efficiently solve it. We present experiments on both synthetic data and real, experimental data with relevance to security applications— thick, dense objects imaged with MeV X-rays. We show that material identification can be efficient and accurate, for example, in a scene with three shells (two copper, one aluminum), our algorithm ran in six minutes on a consumer-level laptop and identified the correct materials as being among the top 10 best matches out of 8,000 possibilities.

36 MATERIALS SCIENCE↗
NASA NTRS1996

Application of Russian Thermo-Electric Devices (TEDS) for the US Microgravity Program Protein Crystal Growth (PCG) Project

Changes in the former Soviet Union have opened the gate for the exchange of new technology. Interest in this work has been particularly related to Thermal Electric Cooling Devices (TED's) which have an application for the Thermal Enclosure System (TES) developed by NASA. Preliminary information received by NASA/MSFC indicates that Russian TED's have higher efficiency. Based on that assumption NASA/MSFC awarded a contract to the University of Alabama in Huntsville (UAH) in order to study the Russian TED's technology. In order to fulfill this a few steps should be made: (1) potential specifications and configurations should be defined for use of TED's in Protein Crystal Growing (PCG) thermal control hardware; and (2) work closely with the identified Russian source to define and identify potential Russian TED's to exceed the performance of available domestic TED's. Based on the data from Russia, it is possible to make plans for further steps such as buying and testing high performance TED's. To accomplish this goal two subcontracts have been released. One subcontract to Automated Sciences Group (ASG) located in Huntsville, AL and one to the International Center for Advanced Studies 'Cosmos' located in Moscow, Russia.

Aksamentov, Valery↗
NASA NTRS2003

An Improved Design for Air Removal from Aerospace Fluid Loop Coolant Systems

Aerospace applications with requirements for large capacity heat removal (launch vehicles, platforms, payloads, etc.) typically utilize a liquid coolant fluid as a transport media to increase efficiency and flexibility in the vehicle design. An issue with these systems however, is susceptibility to the presence of noncondensable gas (NCG) or air. The presence of air in a coolant loop can have numerous negative consequences, including loss of centrifugal pump prime, interference with sensor readings, inhibition of heat transfer, and coolant blockage to remote systems. Hardware ground processing to remove this air is also cumbersome and time consuming which continuously drives recurring costs. Current systems for maintaining the system free of air are tailored and have demonstrated only moderate success. An obvious solution to these problems is the development and advancement of a passive gas removal device, or gas trap, that would be installed in the flight cooling system simplifying the initial coolant fill procedure and also maintaining the system during operations. The proposed device would utilize commercially available membranes thus increasing reliability and reducing cost while also addressing both current and anticipated applications. In addition, it maintains current pressure drop, water loss, and size restrictions while increasing tolerance for pressure increases due to gas build-up in the trap.

Ritchie, Stephen M. C.↗
NASA NTRS2006

Update on the Wide-field Infrared Survey Explorer (WISE)

The Wide-field Infrared Survey Explorer (WISE), a NASA MIDEX mission, will survey the entire sky in four bands from 3.3 to 23 microns with a sensitivity 1000 times greater than the IRAS survey. The WISE survey will extend the Two Micron All Sky Survey into the thermal infrared and will provide an important catalog for the James Webb Space Telescope. Using 1024(sup 2) HgCdTe and Si:As arrays at 3.3, 4.7, 12 and 23 microns, WISE will find the most luminous galaxies in the universe, the closest stars to the Sun, and it will detect most of the main belt asteroids larger than 3 km. The single WISE instrument consists of a 40 cm diamond-turned aluminum afocal telescope, a two-stage solid hydrogen cryostat, a scan mirror mechanism, and reimaging optics giving 5 resolution (full-width-half-maximum). The use of dichroics and beamsplitters allows four color images of a 47' x47' field of view to be taken every 8.8 seconds, synchronized with the orbital motion to provide total sky coverage with overlap between revolutions. WISE will be placed into a Sun-synchronous polar orbit on a Delta 7320-10 launch vehicle. The WISE survey approach is simple and efficient. The three-axis-stabilized spacecraft rotates at a constant rate while the scan mirror freezes the telescope line of sight during each exposure. WISE has completed its mission Preliminary Design Review and its NASA Confirmation Review, and the project is awaiting confirmation from NASA to proceed to the Critical Design phase. Much of the payload hardware is now complete, and assembly of the payload will occur over the next year. WISE is scheduled to launch in late 2009; the project web site can be found at www.wise.ssl.berkeley.edu.

aster↗
NASA NTRS2012

Development of Carbon Dioxide Removal Systems for Advanced Exploration Systems

"NASA's Advanced Exploration Systems (AES) program is pioneering new approaches for rapidly developing prototype systems, demonstrating key capabilities, and validating operational concepts for future human missions beyond Earth orbit" (NASA 2012). These forays beyond the confines of earth's gravity will place unprecedented demands on launch systems. They must not only blast out of earth's gravity well as during the Apollo moon missions, but also launch the supplies needed to sustain a crew over longer periods for exploration missions beyond earth's moon. Thus all spacecraft systems, including those for the separation of metabolic carbon dioxide and water from a crewed vehicle, must be minimized with respect to mass, power, and volume. Emphasis is also placed on system robustness both to minimize replacement parts and ensure crew safety when a quick return to earth is not possible. Current efforts are focused on improving the current state-of-the-art systems utilizing fixed beds of sorbent pellets by seeking more robust pelletized sorbents, evaluating structured sorbents, and examining alternate bed configurations to improve system efficiency and reliability. These development efforts combine testing of sub-scale systems and multi-physics computer simulations to evaluate candidate approaches, select the best performing options, and optimize the configuration of the selected approach, which is then implemented in a full-scale integrated atmosphere revitalization test. This paper describes the carbon dioxide (CO2) removal hardware design and sorbent screening and characterization effort in support of the Atmosphere Resource Recovery and Environmental Monitoring (ARREM) project within the AES program. A companion paper discusses development of atmosphere revitalization models and simulations for this project.

Knox, James C.↗
NASA NTRS2013

Synthetic Foveal Imaging Technology

Apparatuses and methods are disclosed that create a synthetic fovea in order to identify and highlight interesting portions of an image for further processing and rapid response. Synthetic foveal imaging implements a parallel processing architecture that uses reprogrammable logic to implement embedded, distributed, real-time foveal image processing from different sensor types while simultaneously allowing for lossless storage and retrieval of raw image data. Real-time, distributed, adaptive processing of multi-tap image sensors with coordinated processing hardware used for each output tap is enabled. In mosaic focal planes, a parallel-processing network can be implemented that treats the mosaic focal plane as a single ensemble rather than a set of isolated sensors. Various applications are enabled for imaging and robotic vision where processing and responding to enormous amounts of data quickly and efficiently is important.

Monacos, Steve P.↗
NASA NTRS2021

Multi-Angle Imager for Aerosols (MAIA) spectral and radiometric calibration

The Multi-angle Imaging SpectroRadiometer (MISR) was launched as part of NASA’s Terra satellite suite of instruments. Still operational, it makes use of observations from multiple view angles to retrieve aerosol abundance and particle properties. The Multi-Angle Imager for Aerosols (MAIA) instrument improves on this heritage by incorporating polarimetry and an expanded spectral range. Combining these data with surface measurements, the relationship between pollution and human health will be explored. MAIA has just completed camera testing, building on the experience from MISR. Spectral calibration now makes use of a double subtractive monochromator, built with the intent to allow the exit slit output to be uniform in spectral content. For radiometric testing, hardware upgrades have included adding UV lamps to the 1.65 cm (65") integrating sphere, use of a NIST-traceable sphere to establish absolute radiances, and the addition of a UV transfer spectrometer to support characterization of the sphere output from 300 to 2500 nm. During camera build, Newton’s rings were observed in the detector Quantum Efficiency (QE) measurements. This is due to etaloning within the detector itself. Etaloning was also evident in the spectral and radiometric characterizations performed on the completed camera. Spectral metrics, including center wavelength and width, are presented here using a moment’s analysis. This better represents the band properties, particularly in bands where fringing is observed, as compared to a full-width at half-maximum determination. It is believed that the MAIA camera has been carefully characterized, and meet its spectral and radiometric requirements.

Harten, Gerard Van↗
NASA NTRS1976

Small-scale test program to develop a more efficient swivel nozzle thrust deflector for V/STOL lift/cruise engines

The installed performance degradation of a swivel nozzle thrust deflector system obtained during increased vectoring angles of a large-scale test program was investigated and improved. Small-scale models were used to generate performance data for analyzing selected swivel nozzle configurations. A single-swivel nozzle design model with five different nozzle configurations and a twin-swivel nozzle design model, scaled to 0.15 size of the large-scale test hardware, were statically tested at low exhaust pressure ratios of 1.4, 1.3, 1.2, and 1.1 and vectored at four nozzle positions from 0 deg cruise through 90 deg vertical used for the VTOL mode.

Schlundt, D. W.↗
DOE OSTI2024

Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)

The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.

42 ENGINEERING↗
DOE OSTI2025

Surrogate optimization of variational quantum circuits

Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. However, the potential has not yet been realized, with few claims of quantum advantage and high resource estimates, especially due to the need for optimization in the presence of noise. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for VQE or more broad applications of hybrid methods in which optimization is required. To this goal, we look to use modern approaches developed in circuit simulations and stochastic classical optimization, which can be combined to form a surrogate optimization approach to quantum circuits. Using an approximate (classical CPU/GPU) state vector simulator as a surrogate model, we efficiently calculate an approximate Hessian, passed as an input for a quantum processing unit or exact circuit simulator. This method will lend itself well to parallelization across quantum processing units. We demonstrate the capabilities of such an approach with and without sampling noise and a proof-of-principle demonstration on a quantum processing unit utilizing 40 qubits.

Gustafson, Erik J. [RIACS, Mtn. View] (ORCID:00000↗
DOE OSTI2020

Real-time Electromagnetic Transient Simulation of Multi-Terminal HVDC-AC Grids based on GPU

High-fidelity electromagnetic transient (EMT) simulation plays a critical role in understanding the dynamic behavior and fast transients involved in operation, control, and protection of Multi-Terminal dc (MTdc) grids. Here, this paper proposes a cost-effective high-performance real-time EMT simulation platform for large-scale cross-continental MTdc grids based on graphics processing unit (GPU). The proposed simulation platform: i) assembles detailed EMT models of all components within an MTdc-ac grid into a single platform. This setup provides a complete simulation solution to capture fast transient signals required for high-bandwidth controller design and protection studies without any compromise; ii) implements the first GPU-based simulation architecture and corresponding algorithms for MTdc-ac grids with real-time performance at scales of 1s; iii) is highly-efficient and balances the high utilization of GPU resources and low latency required for the simulation; and iv) outperforms the existing central processing unit (CPU)- or digital signal processor (DSP)/field-programmable gate array (FPGA)-based simulators in terms of its higher scalability on large-scale MTdc-ac grids and superior price-performance ratio on the hardware. Accuracy and performance of the proposed platform are evaluated with respect to the reference results from PSCAD/EMTDC environment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Fast Adaptive Neural Control of Resonant Extraction at Fermilab

We present the development of a machine learning (ML) based regulation system for third-order resonant beam extraction in the Mu2e experiment at Fermilab. Classical and ML-based controllers have been optimized using semi-analytic simulations and evaluated in terms of regulation performance and training efficiency. We compare several controller architectures and discuss the integration of neural control into an adaptive framework. We also present progress on surrogate models that predict the controller response given a spill intensity and controller action history. To enable real-time deployment, we report progress on implementing low-latency, edge-based inference suitable for hardware-constrained environments. Our results demonstrate the feasibility and advantages of ML-based control in managing complex, time-varying physical systems, with broader implications for accelerator operations and other domains requiring fast, adaptive regulation.

Berlioz, Jose Rene [Fermilab]↗