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DOE OSTI2022

Development, construction and qualification tests of the mechanical structures of the electromagnetic calorimeter of the Mu2e experiment at Fermilab

The “muon-to-electron conversion” (Mu2e) experiment at Fermilab will search for the Charged Lepton Flavour Violating neutrino-less coherent conversion of a muon into an electron in the field of an aluminum nucleus. The observation of this process would be the unambiguous evidence of physics beyond the Standard Model. Mu2e detectors comprise a straw-tracker, an electromagnetic calorimeter and an external veto for cosmic rays. The calorimeter provides excellent electron identification, complementary information to aid pattern recognition and track reconstruction, and a fast calorimetric online trigger. The detector has been designed as a state-of-the-art crystal calorimeter and employs 1340 pure Cesium Iodide (CsI) crystals readout by UV-extended silicon photosensors and fast front-end and digitization electronics. A design consisting of two identical annular matrices (named “disks”) positioned at the relative distance of 70 cm downstream the aluminum target along the muon beamline satisfies the Mu2e physics requirements.The hostile Mu2e operational conditions, in terms of radiation levels (total ionizing dose of 12 krad and a neutron fluence of 5x1010 n/cm2 @ 1 MeVeq (Si)/y), magnetic field intensity (1 T) and vacuum level (10$^{-4}$ Torr) have posed tight constraints on the design of the detector mechanical structures and materials choice. The support structure of the two 670 crystal matrices employs two aluminum hollow rings and parts made of open-cell vacuum-compatible carbon fiber. The photosensors and service front-end electronics for each crystal are assembled in a unique mechanical unit inserted in a machined copper holder. The 670 units are supported by a machined plate made of vacuum-compatible plastic material. The plate also integrates the cooling system made of a network of copper lines flowing a low temperature radiation-hard fluid and placed in thermal contact with the copper holders to constitute a low resistance thermal bridge. The data acquisition electronics is hosted in aluminum custom crates positioned on the external lateral surface of the two disks. The crates also integrate the electronics cooling system as lines running in parallel to the front-end system.The constraints on the calorimeter mechanical structures design, the development from the conceptual design to the specifications of all the structural components, the status of components production and the components quality assurance tests are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1987

Phase space simulation of collisionless stellar systems on the massively parallel processor

A numerical technique for solving the collisionless Boltzmann equation describing the time evolution of a self gravitating fluid in phase space was implemented on the Massively Parallel Processor (MPP). The code performs calculations for a two dimensional phase space grid (with one space and one velocity dimension). Some results from calculations are presented. The execution speed of the code is comparable to the speed of a single processor of a Cray-XMP. Advantages and disadvantages of the MPP architecture for this type of problem are discussed. The nearest neighbor connectivity of the MPP array does not pose a significant obstacle. Future MPP-like machines should have much more local memory and easier access to staging memory and disks in order to be effective for this type of problem.

White, Richard L.↗

NASA NTRS1988

Problems in characterizing barrier performance

The barrier is a synchronization construct which is useful in separating a parallel program into parallel sections which are executed in sequence. The completion of a barrier requires cooperation among all executing processes. This requirement not only introduces the wait for the slowest process delay which is inherent in the definition of the synchronization, but also has implications for the efficient implementation and measurement of barrier performance in different systems. Types of barrier implementation and their relationship to different multiprocessor environments are described. Then the problem of measuring the performance of barrier implementations on specific machine architecture is discussed. The fact that the barrier synchronization requires the cooperation of all processes makes the problem of performance measurement similarly global. Making non-intrusive measurements of sufficient accuracy can be tricky on systems offering only rudimentary measurement tools.

Jordan, Harry F.↗

DOE OSTI2021

DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS

Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Optimizing inference of segmentation on high-resolution images in MLExchange

MLExchange is a machine learning (ML) operations platform providing web user-interfaces (UIs) for data visualization and analysis pipelines at synchrotron facilities. Among these UIs is the segmentation app which helps synchrotron users utilize ML algorithms to automatically segment high-resolution scientific images with minimal manual annotation effort. In this work, we share code optimizations that significantly speed up the segmentation inference workflow of large data in short time. By optimizing the sequence of CPU-GPU data transfers and introducing CPU parallelization to key operations, we improve the per-device, per-image frame computational efficiency and observe close to 3×$$\times$$ speedup over the original segmentation inference workflow run time when utilizing a single GPU. Further adaptations enabling multi-GPU inference yield more than 40×$$\times$$ speedup with 100 GPUs compared to the optimized single GPU inference workflow. This acceleration of the segmentation inference workflow will provide MLExchange users with easy access to segmentation results with little wait time.

Lu, Shizhao↗

DOE OSTI2024

Control And Optimization Modular Modeling Application For Nuclear Deployment

The purpose of the COMMAND code is to provide a flexible, scalable tool for use in developing, integrating, and testing the technologies necessary for achieving autonomous operations of advanced nuclear reactors. The code enables users to efficiently implement custom simulations and experiments by combining key methods from different software modules. These modules are focused on: modeling and simulation tools, such as nuclear simulation tools used for high-fidelity modeling (e.g., Reactor Excursion and Leak Analysis Program [RELAP5-3D] and Monte Carlo N-Particle [MCNP]); machine learning and optimization tools (e.g., anomaly detection and data-driven modeling techniques); advanced control in its digital, high-performance, and supervisory control forms (e.g., proportional integral derivative (PID) control and model predictive control (MPC); and integration with hardware through industrial communication protocols. To ensure flexibility and scalability, COMMAND was designed to be both modular—the software “pieces” all inherit from generic building blocks and can be combined and connected to create complicated simulations—and high performing—designed for parallel processing, enabling simulations and experiments to take advantage of multi-core computers, servers, and nodes. The code is written in the Python programming language due to the language's popularity, active community, and open-source and cross-platform nature. Maintaining consistency with other simulation tools used within the nuclear energy community, users implement simulations and experiments through text input files, which define components, parameters, connections, etc., through lines of text. Given that COMMAND is written in Python, these input files are native Python scripts, and so use the standard Python structure and formatting. This also enables users to take advantage of Python's extensive package library to develop custom capabilities for their specific use cases.

Faber, Jacob [Idaho National Laboratory (INL), Ida↗

DOE OSTI2020

Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

Here we propose a generalized space-time domain decomposition approach for the physics-informed neural networks (PINNs) to solve nonlinear partial differential equations (PDEs) on arbitrary complex-geometry domains. The proposed framework, named eXtended PINNs ( X P I N N s ), further pushes the boundaries of both PINNs as well as conservative PINNs (cPINNs), which is a recently proposed domain decomposition approach in the PINN framework tailored to conservation laws. Compared to PINN, the XPINN method has large representation and parallelization capacity due to the inherent property of deployment of multiple neural networks in the smaller subdomains. Unlike cPINN, XPINN can be extended to any type of PDEs. Moreover, the domain can be decomposed in any arbitrary way (in space and time), which is not possible in cPINN. Thus, XPINN offers both space and time parallelization, thereby reducing the training cost more effectively. In each subdomain, a separate neural network is employed with optimally selected hyperparameters, e.g., depth/width of the network, number and location of residual points, activation function, optimization method, etc. A deep network can be employed in a subdomain with complex solution, whereas a shallow neural network can be used in a subdomain with relatively simple and smooth solutions. We demonstrate the versatility of XPINN by solving both forward and inverse PDE problems, ranging from one-dimensional to three-dimensional problems, from time-dependent to time-independent problems, and from continuous to discontinuous problems, which clearly shows that the XPINN method is promising in many practical problems. The proposed XPINN method is the generalization of PINN and cPINN methods, both in terms of applicability as well as domain decomposition approach, which efficiently lends itself to parallelized computation. The XPINN code is available on h t t p s : / / g i t h u b . c o m / A m e y a J a g t a p / X P I N N s .

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2006

LDCM Grid Prototype (LGP)

The LGP successfully demonstrated that grid technology could be used to create a collaboration among research scientists, their science development machines, and distributed data to create a science production system in a nationally distributed environment. Grid technology provides a low cost and effective method of enabling production of science products by the science community. To demonstrate this, the LGP partnered with NASA GSFC scientists and used their existing science algorithms to generate virtual Landsat-like data products using distributed data resources. LGP created 48 output composite scenes with 4 input scenes each for a total of 192 scienes processed in parallel. The demonstration took 12 hours, which beat the requirement by almost 50 percent, well within the LDCM requirement to process 250 scenes per day. The LGP project also showed the successful use of workflow tools to automate the processing. Investing in this technology has led to funding for a ROSES ACCESS proposal. The proposal intends to enable an expert science user to produce products from a number of similar distributed instrument data sets using the Land Cover Change Community-based Processing and Analysis System (LC-ComPS) Toolbox. The LC-ComPS Toolbox is a collection of science algorithms that enable the generation of data with ground resolution on the order of Landsat-class instruments.

Weinstein, Beth↗

DOE OSTI2020

Device for controlling additive manufacturing machinery

A computing device for controlling the operation of an additive manufacturing machine comprises a memory element and a processing element. The memory element is configured to store a three-dimensional model of a part to be manufactured, wherein the three-dimensional model defines a plurality of cross sections of the part. The processing element is in communication with the memory element. The processing element is configured to receive the three-dimensional model, determine a path across a surface of each cross section, wherein the path includes a plurality of parallel lines, calculate a power for a radiation beam to scan each of the lines, such that the power varies from line to line non-linearly according to a length of the line, and calculate a scan speed for the radiation beam for each of the lines, such that the scan speed varies line to line non-linearly according to the power of the radiation beam.

Barr, Christian↗

NASA NTRS2008

NASA Tech Briefs, February 2008

Topics discussed include: Optical Measurement of Mass Flow of a Two-Phase Fluid; Selectable-Tip Corrosion-Testing Electrochemical Cell; Piezoelectric Bolt Breakers and Bolt Fatigue Testers; Improved Measurement of B(sub 22) of Macromolecules in a Flow Cell; Measurements by a Vector Network Analyzer at 325 to 508 GHz; Using Light to Treat Mucositis and Help Wounds Heal; Increasing Discharge Capacities of Li-(CF)(sub n) Cells; Dot-in-Well Quantum-Dot Infrared Photodetectors; Integrated Microbatteries for Implantable Medical Devices; Oxidation Behavior of Carbon Fiber-Reinforced Composites; GIDEP Batching Tool; Generic Spacecraft Model for Real-Time Simulation; Parallel-Processing Software for Creating Mosaic Images; Software for Verifying Image-Correlation Tie Points; Flexcam Image Capture Viewing and Spot Tracking; Low-Pt-Content Anode Catalyst for Direct Methanol Fuel Cells; Graphite/Cyanate Ester Face Sheets for Adaptive Optics; Atomized BaF2-CaF7 for Better-Flowing Plasma-Spray Feedstock; Nanophase Nickel-Zirconium Alloys for Fuel Cells; Vacuum Packaging of MEMS With Multiple Internal Seal Rings; Compact Two-Dimensional Spectrometer Optics; and Fault-Tolerant Coding for State Machines.

Source record↗

DOE OSTI2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these models, especially to make predictions at large output resolutions (≥1024×1024). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods. We show excellent scalability of this framework on both cloud as well as HPC clusters, and report on the interplay between bandwidth, network topology and bare metal vs cloud. We deploy this approach to train generative models of sizes hitherto not possible, showing that neural PDE solvers can be viably trained for practical applications. We also demonstrate that distributed higher-order optimization methods are 2-3× faster than stochastic gradient-based methods and provide minimal convergence drift with higher batch-size.

PDEs↗

DOE OSTI2025

Overview on Current Activities of Conduction-Cooled SRF Accelerators and their Applications

When Nb3Sn was reintroduced to the SRF community as an alternative to pure niobium, one key motivation has been to reduce the cryogenic requirements of new and existing accelerators by shifting from 2 K to 4 K operation. Meanwhile, a variety of implementations beyond research machines are being explored. The combination of Nb3Sn with conventional cryocoolers, enabling cryogen-free operation, has paved the way for the development of compact, standalone systems suitable for applications far beyond research, such as enhancing the durability of synthetics via crosslinking or sterilizing food and medical equipment, as well as environmental cleanup when it comes to decontaminating liquid and solid waste material. So, while fundamental R&D continues to refine Nb3Sn resonators, exploring improvements such as replacing the niobium substrate with copper, parallel research efforts are investigating how the increased beam power provided by SRF could expand the commercial use of electron beams. This presentation aims to deliver a comprehensive overview of ongoing research efforts to harness the benefits of SRF through Nb3Sn and conduction cooling.

Vennekate, John [Thomas Jefferson National Acceler↗

NASA NTRS1993

Dynamic programming on a shared-memory multiprocessor

Three new algorithms for solving dynamic programming problems on a shared-memory parallel computer are described. All three algorithms attempt to balance work load, while keeping synchronization cost low. In particular, for a multiprocessor having p processors, an analysis of the best algorithm shows that the arithmetic cost is O(n-cubed/6p) and that the synchronization cost is O(absolute value of log sub C n) if p much less than n, where C = (2p-1)/(2p + 1) and n is the size of the problem. The low synchronization cost is important for machines where synchronization is expensive. Analysis and experiments show that the best algorithm is effective in balancing the work load and producing high efficiency.

Edmonds, Phil↗

NASA NTRS1998

A Cognitive-System Model for En Route Air Traffic Management

NASA Ames Research Center has been engaged in the development of advanced air traffic management technologies whose basic form is cognitive aiding systems for air traffic controller and flight deck operations. In the design and evaluation of such systems the dynamic interaction between the airborne aiding system and the ground-based aiding systems forms a critical coupling for control. The human operator is an integral control element in the system and the optimal integration of human decision and performance parameters with those of the automation aiding systems offers a significant challenge to cognitive engineering. This paper presents a study in full mission simulation and the development of a predictive computational model of human performance. We have found that this combination of methodologies provide a powerful design-aiding process. We have extended the computational model Man Machine Integrated Design and Analysis System (N13DAS) to include representation of multiple cognitive agents (both human operators and intelligent aiding systems), operating aircraft airline operations centers and air traffic control centers in the evolving airspace. The demands of this application require the representation of many intelligent agents sharing world-models, and coordinating action/intention with cooperative scheduling of goals and actions in a potentially unpredictable world of operations. The operator's activity structures have been developed to include prioritization and interruption of multiple parallel activities among multiple operators, to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. We have exercised this model in a multi-air traffic sector scenario with potential conflict among aircraft at and across sector boundaries. We have modeled the control situation as a multiple closed loop system. The inner and outer loop alerting structure of air traffic management has many implications that need to be investigated to assure adequate design. First, there are control and stability factors implicit in the design. As the inner loop response time approaches that of the outer loop, system stability may be compromised in that controllers may be solving a problem the nature of which has already been changed by pilot action. Second, information exchange and information presentation for both air and ground must be designed to complement as opposed to compete with each other. Third, the level of individual and shared awareness in trajectory modification and flight conformance needs to be defined. Fourth, the level of required awareness and performance impact of mixed fleet operations and failed-mode recovery must be explored.

Corker, Kevin M.↗

NASA NTRSDate not supplied

Dynamic Radioisotope Power Systems Development Status and Path to Flight

Dynamic power conversion offers the potential to produce Radioisotope Power Systems (RPS) that generate higher power outputs and utilize the Pu-238 radioisotope more efficiently. Additionally, dynamic power conversion offers the potential of producing generators with minimal degradation resulting in more power at the end of the mission, when the power is needed. Dynamic power conversion technologies being developed for space applications include the Stirling and Brayton thermodynamic cycle machines. Machines can be built based on these cycles while eliminating wear mechanisms of the moving components, enabling long design life necessary for space missions. The Dynamic Radioisotope Power Systems (DRPS) project at NASA Glenn Research Center (Glenn Research Center) is pursuing the realization of this type of power source on a flight mission. The project currently has three convertor development contracts that will deliver prototype hardware in 2020. This hardware will undergo a gamut of experimental performance verification efforts at NASA GRC. In parallel, the project has also initiated generator design efforts based on these underlying convertor options, and is also on track to build an in-house version of a generator for laboratory system-level testing. The project is also funding control electronics technology, which are necessary to convert alternating current from the dynamic devices to direct current for use by a spacecraft. A lunar mission is being targeted as the first use of this new technology, as DRPS enables a wide range of high-return scientific missions on the moon, while the mission being short in duration (2 years rather than 10 years for an outer planets mission).

Salvatore M Oriti↗

NASA NTRSDate not supplied

Dynamic Radioisotope Power Systems Status and Path to Flight

Salvatore Oriti↗

NASA NTRS1990

Simulation technology - A key to effective man-machine integration for future combat rotorcraft systems

The utilization of advanced simulation technology in the development of the non-real-time MANPRINT design tools in the Army/NASA Aircrew-Aircraft Integration (A3I) program is described. A description is then given of the Crew Station Research and Development Facilities, the primary tool for the application of MANPRINT principles. The purpose of the A3I program is to develop a rational, predictive methodology for helicopter cockpit system design that integrates human factors engineering with other principles at an early stage in the development process, avoiding the high cost of previous system design methods. Enabling technologies such as the MIDAS work station are examined, and the potential of low-cost parallel-processing systems is indicated.

Kerr, Andrew W.↗

NASA NTRS2006

Development of Laser Fabricated Ti-6Al-4V

Laser Engineered Net Shaping (LENS) depositions with Ti-6Al-4V gas-atomized powder were accomplished at five different temperatures, ranging from 30 to 400 C, imposed on the base plate. These base plate temperatures were employed in an effort to relieve stresses which develop during the deposition. Warpage of the base plate was monitored. Only a slight decline in warpage was observed as the base plate temperature was increased. Results indicate that substrate temperatures closer to the stress relief minimum of 480 C would relieve deposition stresses, though process parameters would likely need to be modified to compensate for the higher base plate temperature. The compositions of the as-received powder and the LENS deposited material were chemically analyzed. The oxygen content of the LENS material was 0.154 wt.% which is less than the maximum impurity limit of 0.2 percent for commercial Ti-6Al-4V alloys, but is over the limit allowed in ELI grade (0.13 percent). The level of oxygen in the commercial base plate used was only 0.0635 percent. Tensile specimens were machined from the LENS deposited material and tested in tension at room temperature. The ultimate and yield tensile stresses of the LENS material were about 1200 and 1150 MPa respectively, which is about 20 percent higher than the strengths of wrought Ti-6Al-4V. The higher strength of the LENS material was due to its fine structure and high oxygen content. The LENS deposits were not fully dense; voids were frequent at the interfaces between deposited layers. These dispersed sheets of voids were parallel to the longitudinal axis of the resulting tensile specimens. Apparently there was sufficient continuous, fully dense material longitudinally to enable the high strengths. Ductility was low in the LENS material. Percent elongation at failure in the LENS material was near 4 percent, which is less than half of what is usually expected from Ti-6Al-4V. The low ductility was caused by high oxygen levels, and the presence of voids. It is likely that the relatively high scan speeds used in our depositions contributed to the lack of full density in our LENS material.

deGroh, Henry C., III↗