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NASA NTRS2018

Evaluating the Suitability of Commercial Clouds for NASA's High Performance Computing Applications: A Trade Study

NASA’s High-End Computing Capability (HECC) Project is periodically asked if it could be more cost effective through the use of commercial cloud resources. To answer the question, HECC’s Application Performance and Productivity (APP) team undertook a performance and cost evaluation comparing three domains: two commercial cloud providers, Amazon and Penguin, and HECC’s in-house resources—the Pleiades and Electra systems. In the study, the APP team used a combination of the NAS Parallel Benchmarks (NPB) and six full applications from NASA’s workload on Pleiades and Electra to compare performance of nodes based on three different generations of Intel Xeon processors—Haswell, Broadwell, and Skylake. Because of export control limitations, the most heavily used applications on Pleiades and Electra could not be used in the cloud; therefore, only one of the applications, OpenFOAM, represents work from the Aeronautics Research Mission Directorate and the Human and Exploration Mission Directorate. The other five applications are from the Science Mission Directorate.

High Performance↗

NASA NTRS2010

Report of the 1st SMAP Applications Workshop

The NASA Earth Science Division has made a commitment to “discover and demonstrate applications that inform resource management, policy development and decision making” (NASA Earth Science Division Applied Sciences Program, Program Strategy, 2010–2015). The NASA mission teams have the opportunity to take a lead role in meeting this challenge. A primary goal of the SMAP mission is to engage SMAP end users and build broad support for SMAP applications through a transparent and inclusive process. Toward this goal, the SMAP mission formed an open-community SMAP Applications Working Group (ApplWG) with over 150 members. The ApplWG held the first SMAP Applications Workshop on September 9–10, 2009, at the National Oceanic and Atmospheric Administration (NOAA) in Silver Spring, Maryland. Workshop attendees represented state and federal agencies, operational centers focused on natural hazards and disasters, international organizations, and academia. Introductory speakers from NOAA, US Geological Survey (USGS), US Department of Agriculture (USDA), Environment Canada, and US Department of Defense (DoD) addressed applications that included floods and droughts, early famine warning, crop assessments, human health, and defense.

O’Neill, P.↗

NASA NTRSDate not supplied

Application of ML/AI for Identifying Earth Science Datasets in Research Publications

NASA Data Active Archive Centers, or DAACs, ingest, store and distribute data acquired from satellites, ground systems as well as modelling data. These data are organized by the datasets, each presenting collection of files usually associated with the certain mission, instrument, processing level, parameter(s), algorithm and/or model. The number of datasets offered by a single DAAC to the public varies. GES DISC, for example, currently offers for public use approximately ~1,300 datasets. While each publicly offered dataset comes with supporting documentation, it is challenging for novice and even experienced scientists to navigate among the datasets that offer similar parameters to find the datasets for their particular research application. Supplying dataset documentation with the scientific paper citations that refer to that dataset provides means for the dataset users to educate themselves with the application research that dataset is being used in. Collecting citations of the papers that use the datasets for their research yield valuable insights into application areas of those datasets, information about usage of the dataset groups for specific applications and those application topics. It also gives insights into the “deep metrics” of the dataset usage, as opposed to the common metrics of the dataset usage such as number of users who downloaded the dataset files and volumes of downloaded data. Association of a certain scientific paper with the dataset(s) presents a challenge because most of the paper authors do not properly cite the datasets, datasets usually have cryptic names and Digital Object Identifiers (DOIs) that are used for dataset identification were assigned to the datasets only few years ago. Simple Google or online library search do not provide even meaningful fraction of the results when performed by the dataset name or DOI, however they provide too many results when the search is done by more broader terms such as mission and instrument names. Attempts to create an AI system capable to identify dataset in the scientific papers have already been made using neural networks classifiers on the basis of the dataset mission, instrument and variable name. This method was applied to NASA SEDAC, which has 41 datasets in total. In GES DISC there can be as many as ~100 datasets per mission/instrument with some of the datasets consisting of multiple variables so there is a need for more differentiating parameters for dataset identification in the paper. The approach we are currently investigating is creating AI classifiers that are based on multiple dataset features, or keywords, extracted from the NASA Earthdata Common Dataset Repository (CMR). The features are weighted based on how precisely they can identify a dataset. The classifier uses preprocessed paper text as input and searches for the CMR datasets whose feature sets are the closest to the feature sets contained in the paper. The challenges of dataset identification include variety of ways the paper authors describe the datasets in their papers and incomplete tagging of the CMR dataset description (DIFs).

Irina Gerasimov↗

NASA NTRSDate not supplied

Advancement of Novel Additively Manufactured Alloys for Space Applications

NASA has been involved in the development and maturation of metal additive manufacturing (AM) for space applications since the late 2000’s. Several efforts have focused on the understanding of AM processes through material characterization and testing, standards development, component fabrication, and infusion into development and flight applications. While many common aerospace alloys have been and continue to be a focus of ongoing development, the need for custom-alloy developments for high performance applications enabled by AM processes has been realized. The applications being targeted are liquid rocket engines with high heat fluxes, high pressure, and that utilize propellants such as hydrogen, which can degrade the alloy. NASA has recently focused on the development and advancement of novel alloy advancement using AM for use in these harsh environments, such as GRCop-42, GRCop-84, NASA HR-1, and JBK-75. These alloys have been evaluated using the laser powder bed fusion (L-PBF) and laser powder directed energy deposition (LP-DED) processes. The results from these processes have demonstrated that AM can enable rapid development of new alloy systems that can yield higher performances. These alloys have undergone the fundamental metallurgical evaluations, heat treatment study, and microstructure characterization and mechanical testing campaign. This, combined with direct application-specific component fabrication and hot-fire testing, enabled the increase of the Technology Readiness Level (TRL). This presentation will provide a background and overview of these AM-enabled novel alloys, AM processing development including metallurgical and mechanical property studies. It will also cover the latest advancement in the parallel component development and testing and future developments. The goal of these alloy development is to allow for technology infusion into NASA and commercial spaceflight missions as well as to establish and sustain the needed commercial AM supply chain.

Additive Manufacturing↗

NASA NTRSDate not supplied

Advancement of Metal Additive Manufacturing Processes and Alloys for Rocket Propulsion Applications

NASA has been involved in the development and maturation of metal additive manufacturing (AM) for space applications since the 2000’s. Several efforts have focused on the understanding of AM processes through material characterization and testing, standards development, component fabrication, and infusion into development and flight applications. While many common aerospace alloys have been and continue to be a focus of ongoing development, the need for custom-alloy developments for high performance applications enabled by various AM processes has been realized. The applications being targeted are liquid rocket engines with high heat fluxes, high pressure, and that utilize propellants such as hydrogen, which can degrade the alloy. NASA has recently focused on the development and advancement of novel alloy advancement using AM for use in these harsh environments, such as GRCop-42, GRCop-84, NASA HR-1, and JBK-75. These alloys have been evaluated using powder bed fusion (PBF), directed energy deposition (DED), and solid-state AM processes. The results from these processes have demonstrated that AM can enable rapid development of new alloy systems that can yield higher performances across various metal AM processes. These alloys have undergone the fundamental metallurgical evaluations, heat treatment study, and microstructure characterization and mechanical testing campaign. This, combined with direct application-specific component fabrication and hot-fire testing, enabled the increase of the Technology Readiness Level (TRL). This presentation will provide a background and overview of various AM-enabled novel alloys, a comparison across the AM processes, and development including metallurgical and mechanical property studies. It will also cover the latest advancement in the parallel component development and testing and future developments. The goal of these alloy development and use of various AM processes is to allow for technology infusion into NASA and commercial spaceflight missions as well as to establish and sustain the needed commercial AM supply chain.

Additive Manufacturing↗

NASA NTRSDate not supplied

Metal Additive Manufacturing Developments for Propulsion Applications

Additive Manufacturing↗

NASA NTRSDate not supplied

A Flat-Panel 8x8 Sequentially Rotated Wideband Microstrip Patch Phased Array Antenna for K/Ka-band 6U CubeSat Communications Applications

Flat-panel electronically-scanned phased arrays have recently become a mainstream technology in the areas of radar and satellite applications, particularly due to their low profile and not having to steer the beams mechanically, both of which helps in realizing a lightweight phased array solution that also occupies a smaller volume. Typically for satellite applications, circularly polarized element radiators are often employed in order to deal with the signal fading problem. In this project, the main objective was to design a flat-panel phased array solution for 6U CubeSat communication applications. As for the required specifications, the array should have two operating bands: 22.55–23.55 GHz and 25.5–27.5 GHz, which span part of the K and Ka bands. For both these bands, the panel should be able to radiate dual circular polarization, in both transmit and receive modes, and with a stable gain performance. Since the individual bands are very closely spaced, it was realized that a single wideband design would be a better solution than a closely spaced dual-band design. Thus, a stacked microstrip patch configuration was selected for the element radiator design, which is known to be wideband. Now, it is rather difficult to realize both wide impedance and axial ratio bandwidths simultaneously, and at high frequencies, without complicating the element stacked patch design.. As a result, a sequential rotation technique wasemployed to significantly improves the axial ratio bandwidth and circular polarization purity of an array, all the while improving the pattern symmetry (P. S. Hall, “Application of Sequential Feeding to Wide Bandwidth, Circularly Polarised Microstrip Patch Arrays,” IEE Proc. H (Microw., Antennas Propag.), vol. 136, no. 5, pp. 390–398, Oct. 1989). The designed element radiator is a circularly-polarized probe-fed stacked patch antenna, which has a 10-dB matching bandwidth of 22.3– 30 GHz, with only a 5.14% axial ratio bandwidth around the center frequency of the required band, i.e., ~25 GHz. However, the resulting array demonstrates a very low axial ratio (≪ 3 dB) over the entire bandwidth, due to the application of the sequential rotation technique. The 8×8 array was able to scan up to ±51° and ±40° at frequencies 23.05 GHz and 26.5 GHz (center frequencies of the lower and upper bands), respectively, while maintaining a ≤ 3 dB axial ratio and a ≤ 3 dB gain drop from the corresponding peak broadside value at both the principle and the diagonal radiation planes. The designed layout for the 8×8 phased array prototype is shown in Fig. 1, which is currently undergoing fabrication and RF assembly. The antenna will be tested in the far-field anechoic chambers of the Antenna and Microwave Laboratory (AML) at San Diego State University and at NASA Glenn Research Center (GRC).

5G↗

NASA NTRSDate not supplied

Maturation of Additive Manufactured Aerospace Alloys and Development of Mechanical and Thermophysical Properties for Space Applications

The National Aeronautics and Space Administration (NASA) has been involved in the development and maturation of metal additive manufacturing (AM) for space applications since the late 2000’s. Several efforts have focused on the understanding of AM processes through design optimization, component fabrication, material characterization, testing, standards development, and infusion into propulsion development for flight applications. NASA and partners have matured commonly used aerospace alloys from various alloy families (Nickel, Steel, Iron, Aluminum, and Titanium) and AM processes. An extensive effort has been ongoing between NASA, industry partners, and academia to complete detailed AM process and heat treatment characterization, in addition to generating temperature-dependent mechanical and thermophysical properties. This presentation highlights the characterization and property results using laser powder bed fusion (L-PBF) and laser powder directed energy deposition (LP-DED) processes among various alloys. In addition to commonly used alloys, there is a need for ongoing AM optimized alloys using integrated computational materials engineering (ICME) and process development for high performance applications. The applications targeted are launch vehicles, liquid rocket engines, advanced propulsion systems, advanced power systems, and in-space propulsion with high heat fluxes, high pressure, and that utilize propellants such as hydrogen, which can degrade alloys. This presentation will also discuss some of the ongoing novel alloy development and maturation using AM for use in these harsh environments, such as GRCop-42, GRCop-84, NASA HR-1, and C-103. The results from these processes have demonstrated that AM can enable rapid development and new AM optimized alloys can yield higher performance. These alloys have undergone modeling, fundamental metallurgical evaluations, heat treatment studies, and microstructure characterization and mechanical testing campaigns. This, combined with direct application-specific component fabrication and hot-fire testing, can enable the increase of the Technology Readiness Level (TRL) through high duty-cycle testing. This presentation provides a background and overview of these various common and AM-enabled alloys and processing developments including the initial effort related to metallurgical, mechanical, and thermophysical property studies. It also covers the latest advancement in the parallel component development, hot-fire testing, and future developments for these alloys.

Additive Manufacturing↗

NASA NTRS2022

Space Applications of a Trusted AI Framework: Experiences and Lessons Learned

Artificial intelligence (AI), which encompasses machine learning (ML), has become a critical technology due to its well-established success in a wide array of applications. However, the proper application of AI remains a central topic of discussion in many safety-critical fields. This has limited its success in autonomous systems due to the difficulty of ensuring AI algorithms will perform as desired and that users will understand and trust how they operate. In response, there is growing demand for trustability in AI to address both the expectations and concerns regarding its use. The Aerospace Corporation (Aerospace) developed a Framework for Trusted AI (henceforth referred to as the framework) to encourage best practices for the implementation, assessment, and control of AI-based applications. It is generally applicable, being based on terms and definitions that cut across AI domains, and thus is a starting point for practitioners to tailor to their particular application. To help demonstrate how the framework can be tailored into mission assurance guidance for the space domain, Aerospace sought the involvement of the Jet Propulsion Laboratory (JPL) to engage with actual examples of AI-based space autonomy.

Kaufman, James↗

NASA NTRS2023

Development of Directed Energy Deposited NASA HR-1 to Optimize Properties for Liquid Rocket Engine Applications

Metal additive manufacturing (AM) processes have been demonstrated to be effective at reducing costs and lead times associated with fabrication of complex propulsion component designs. Laser powder directed energy deposition (LP-DED) is a metal AM technology that has effectively been used to produce a variety of parts for liquid rocket engine applications. The LP-DED technology has enabled new designs and materials for these applications. The National Aeronautics and Space Administration (NASA) has identified the need to develop and advance new materials in unique engine applications such as hydrogen environments. One such metal alloy being developed for these applications is NASA HR-1. A high-strength Fe-Ni based superalloy, NASA HR-1 was designed to resist high pressure hydrogen environment embrittlement, oxidation, and corrosion. Enabled by LP-DED, NASA HR-1 has completed build and heat treatment optimization, characterization, mechanical and thermophysical testing, and hot-fire testing to demonstrate its capability as an alloy used in liquid rocket engine applications. The evolution of the alloy through material characterization analysis and mechanical testing results from development and optimization efforts will be presented.

additive manufacturing↗

NASA NTRSDate not supplied

Establishing and Maintaining the Digital Thread of Additively Manufactured Materials and Applications

Additive Manufacturing (AM) and Integrated Computational Materials Engineering (ICME) are complementary enabling technologies for design and manufacturing of “fit-for-purpose” materials. Both technologies will impact rapid material design, reduction in cost- and time-to-market for new applications, and discovery and implementation of new materials. An ICME approach to design, however, requires experimentally validated material models at multiple length and time scales, an integrated framework that can connect analysis tools with one another to ensure the digital thread of an application is maintained, and the manufacturing (e.g., AM) capability to leverage processing-structure-property-performance (PSPP) relationships to achieve spatially varying material properties where desired. AM enables the implementation of the design of an optimized, spatially varying microstructure through careful selection of the processing parameters used during an additively manufactured build. In order to establish these PSPP relations, a large amount of data is necessary, and that data must be properly captured, analyzed and maintained in an information management system that can establish the required traceability between various aspects of the design process to ensure an application’s digital thread is maintained (from design to end of life). Such an information management system must be able to capture feedstock material pedigree, resulting microstructure from various build parameters, subsequent mechanical properties derived from testing, developed material models, and enable spatial variations in material assignment in an engineering application. Furthermore, the information management system should be easily integrated with traditionally engineered materials in a single, centralized platform to enable an ICME optimization tool to explore both types of manufacturing processes. At NASA GRC, a robust, 21st century materials information management system has been previously developed with a focus towards enabling ICME. In this work, GRC’s ICME schema is extended to accommodate additively manufactured materials, enabling storage of both traditionally and additively manufactured materials in the same construct. The methodology for properly capturing additively manufactured materials across the entire material lifecycle is presented, following the previously established database best practices, as a potential framework for establishing PSPP relationships for additively manufactured materials and applying them to engineering applications.

Data management↗

NASA NTRSDate not supplied

Managing the Digital Thread for Structural Applications With Fit for Purpose Materials

With the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing - internal structure - property - performance envelope, along with the capturing and storing of the associated material and model information across its lifecycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Consequently, at NASA Glenn Research Center a robust information management system that manages the digital thread across the full material life (i.e., capture, analysis, maintenance, and dissemination of data) cycle directed at the design of ‘fit-for-purpose materials’ is under development. To this end the Application Table has been incorporated within NASA Glenn Research Center’s ICME Information Management framework within the ANSYS Granta MI tool. The Application Table provides a place where material and structural application information/requirements can be linked to marry the “design-the-material” (structural engineering) and the “design-with-material” (material science) paradigms and thereby enable application-driven design and optimization of materials and structures. In additional several associated toolsets, specifically: AIMAOS (Automated Information Management Across Organizations and Scales), Py MILab, and JARIMIS (Just A Rather Intelligent Material Interrogation System) are also under development to assist in the judicious automation of this process. AIMOAS offers users an interactive graphical user interface for connecting material information management systems with both commercial and in-house simulation tools at various length scales to enable such automation in the handoff across scales and maintenance of material digital twins and the digital thread. Py MILab, is an automatic framework for the capture, analysis, maintenance, and storage of material test data. Py MILab uses a modular approach for capturing raw data, analyzing the data, and storing the data in a database, interfaced by neutral file structures, to promote plug-and-play capabilities for various analysis types. Finally, JARIMIS is an expert system that integrates various materials informatics tools (e.g., MicroNet, Surrogate ML models, ANSYS Granta MI, etc.) to enable inverse design of materials and facilitate the application of machine learning (ML) and data science with human in the loop decision making to rapidly discover and optimize new materials.

Digital Transformation↗

DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗

DOE OSTI2024

FREDA: A Web Application for the Processing, Analysis, and Visualization of Fourier‐Transform Mass Spectrometry Data

The high-resolution measurement capability of Fourier-transform mass spectrometry (FT-MS) has made it a necessity for exploring the molecular composition of complex organic mixtures, like soil, plant, aquatic, and petroleum samples. This demand has driven a need for informatics tools to explore and analyze FT-MS data in a robust and reproducible manner. FREDA is an interactive web application developed to enable spectrometrists to format, process, and explore their FT-MS data without the need for statistical programming expertise. FREDA was built to explore outputs from a molecular identification tool, like CoreMS, and provide a suite of methods to filter data, compute chemical properties of peaks, statistically compare samples and groups of samples, conduct exploratory data analysis, and download the results with a report detailing all steps conducted. To demonstrate the utility of FREDA, an example analysis was conducted using FT-MS data from a soil microbiology study of samples collected in two different soil depths at the Sphagnum bog forest north of Grand Rapids, Minnesota. Differences between the two depths are observed using Kendrick, Gibbs free energy, and van Krevelen plots. G-tests are used to quantify a significant difference between the groups. All analyses and plotting are conducted using only the FREDA application. FREDA is an open-source and readily available web application that allows users to explore and make statistically valid conclusions about their FT-MS data. The application is available online (https://map.emsl.pnnl.gov/app/freda) with a tutorial web series (https://youtu.be/k5HLE2kNSBY?si=yB6sGoyvzxrFf5MP) and freely accessible code on Github (https://github.com/EMSL-Computing/FREDA).

47 OTHER INSTRUMENTATION↗

NASA NTRS2005

Design for ASIC reliability for low-temperature applications

In this paper, we present a methodology to design for reliability for low temperature applications without requiring process improvement. The developed hot carrier aging lifetime projection model takes into account both the transistor substrate current profile and temperature profile to determine the minimum transistor size needed in order to meet reliability requirements. The methodology is applicable for automotive, military, and space applications, where there can be varying temperature ranges. A case study utilizing this methodology is given to design for reliability into a custom application-specific integrated circuit (ASIC) for a Mars exploration mission.

reliability↗

NASA NTRSDate not supplied

Best Practices, Lessons Learned, and Examples on the Application of Damage Tolerance in Space Structures

Damage tolerance is an important consideration in space structures applications. The intent of damage tolerance is to demonstrate that the structure is robust to the presence of flaws over the service life of the space vehicle. Damage tolerance requirements in documents such as those in NASA, AIAA, and ISO can be challenging to implement. The intent of this paper is three-fold: (1) Provide best practices in the application of damage tolerance requirements for space applications, (2) Provide lessons learned for each of class of hardware, (3) Provide examples of challenging situations encountered in the application of damage tolerance. Examples cover additive manufacturing, composite overwrapped pressure vessels, pressurized structures, liquid rocket engines, thermal protection systems, and other classes of hardware. The philosophy and limitations of leak before burst and proof test logic are also discussed. Finally, a discussion on elastic-plastic fracture mechanics with comparisons to test data will be presented.

Best Practices↗

DOE OSTI2025

Recent Progress on Cerium Oxide‐Based Nanostructures for Energy and Environmental Applications

Cerium oxide (CeO 2 ) photo/electrocatalysts for energy storage and environmental applications have attracted considerable interest because of stable crystal structure, low toxicity/cost, superior chemical stability, stable redox (Ce 3+ /Ce 4+ ) pairs, abundant oxygen defects, and capablility for intense interaction with other materials. However, the wide bandgap and poor conductivity lower the CeO 2 photo/electrocatalytic and energy storage performances. To overcome these limitations, various modification strategies (tuning morphology, doping or loading of metal nanoparticles, and heterostructures) have been applied for the improvement of photocatalytic (removal of organic contaminants from water/wastewater and H 2 production and CO 2 reduction reactions) efficiency, electrocatalytic (hydrogen/oxygen evolution reactions and CO 2 reduction reactions), and energy storage performances (supercapacitor) of CeO 2 ‐based materials. Herein, the recent progress of CeO 2 ‐based materials for electro(photo)catalysis and energy storage applications has been discussed. The challenges and possible direction of CeO 2 ‐based materials for electro(photo)catalysis and energy storage applications have been emphasized. Furthermore, this comprehensive review is expected to advance the design of CeO 2 ‐based materials and their applications in electro(photo)catalysis and energy.

Ray, Schindra Kumar [Department of Chemistry North↗

DOE OSTI2025

Small scale electron linear accelerators for industrial applications

Linear accelerators (linacs), producing electron or X-ray radiation in the MeV range, are critical tools for industrial irradiation, medical device sterilization, food pasteurization, non-destructive testing, security, medical, and many other applications. Many of these applications require compact and flexible radiation sources. In this paper, we present new technologies for small-scale electron linear accelerators and examples of their practical implementations. Furthermore, these developments include low energy accelerators with self-shielding options and form factors, ranging from cabinet-size to hand-portable generators; medium-energy accelerators for novel radiotherapy and security applications; and high-energy 10 MeV linacs capable of reaching 20–35 kW beam power for emerging industrial and phytosanitary applications

43 PARTICLE ACCELERATORS↗