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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstrated in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

The ICF National Diagnostic Plan (NDP) 9/19/17

A major goal of the Stockpile Stewardship Program (SSP) is to deliver validated numerical models, benchmarked against experiments that address relevant and important issues and provide data that stress the codes and our understanding. DOENNSA has made significant investments in major facilities and high-performance computing to successfully execute the SSP. The more information obtained about the physical state of the plasmas produced, the more stringent the test of theories, models, and codes can be, leading to increased confidence in our predictive capability. To fully exploit the world-leading capabilities of the ICF program, a multi-year program to develop and deploy advanced diagnostics has been developed by the expert scientific community. To formalize these activities NNSA’s Acting Director for the Inertial Confinement Fusion Program directed the formation and duties of the National Diagnostics Working Group (NDWG) in a Memorandum 11/3/16 (Appendix A). The NDWG identified eight transformational diagnostics, shown in Table 1, that will provide unprecedented information from experiments in support of the SSP at NIF, Z and OMEGA. Table 1 shows how the missions of the SSP experiments including materials, complex hydrodynamics, radiation flow and effects and thermo-nuclear burn and boost will produce new observables, which will be measured using a variety of largely new diagnostic technologies used in the eight transformational diagnostics. The data provided by these diagnostics will validate and improve the physics contained within the SSP’s simulations and both uncover and quantify important phenomena that lie beyond our present understanding.

42 ENGINEERING↗
DOE OSTI2023

Detectors and diagnostics for multiprobe radiography using high-energy short-pulse lasers

Multiprobe radiography requires the following diagnostic capabilities at minimum: radiographic source diagnostics, spectroscopy, imaging, and tomography diagnostics. Depending on the repetition rate of the radiation sources, the diagnostics may be a single-shot diagnostic, such as photographic (radiochromic and X-ray films for high energy photons) films, or a multiple-shot diagnostic for repetitive source operations, such as a high-speed framing camera. Due to the drastically different detector physics of electrons, photons at different energies (keV to 10s of MeV in the radiography context), heavy ions such as protons, alphas, and neutrons, see, for example, Ref. [1, 2] for an overview, different types or configurations of detectors and constructions may be required for multiprobe radiography applications. Some of the common detector metricsinclude, independent of the particle/photon species, sensitivities or efficiency, energy resolution, spatial or temporal resolutions and others. Some unique detection scenarios arise in a multiprobe radiography setting, for example, crosstalk mitigation and particle identification (ID) when X-ray and higher energy photons, relativistic electrons, neutrons, other hadrons, and heavy particles coexist at the detector location.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

ARM data-oriented metrics and diagnostics package for climate model evaluation (ARM-DIAGS-V3) version 3

A Python-based metrics and diagnostics package is currently being developed by the ARM Infrastructure Team at Lawrence Livermore National Laboratory to facilitate the use of long-term high frequency measurements from the ARM program in evaluating the regional climate simulation of clouds, radiation, precipitation, and aerosols. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The CMIP model data sets are also included in the package to enable model inter-comparison as demonstrated in Zhang et al. (2018) and Zhang et al. (2020). The mean of the CMIP model can be served as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, aerosol optical depth, and radiative fluxes, with plan to extend to more fields, such as the evaluation of model simulated aerosol physicochemical properties and cloud microphysics properties. Process-oriented diagnostics focusing on aerosol, cloud, and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. In addition to the Southern Great Plains (SGP), North Slope of Alaska (NSA) and Tropical Western Pacific (TWP) atmospheric observatories in the ARMDIAGS version 2.0, the version 3.0 package have extended to the data collected at the ARM Eastern North Atlantic (ENA) atmospheric observatory and the Observation and Modeling of the Green Ocean Amazon (GOAMAZON) field campaign. The metrics and diagnostics package are currently built upon standard Python libraries and additional Python packages developed by DOE (CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first provide an overview of major metrics in section 2. The input data, which constitutes the core content of the metrics and diagnostics package, is summarized in section 3. A user's guide documenting the workflow/structure of the version 3.0 codes and including step-by-step instruction for running the package is described in section 4.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine-Learning-Based Rotating Detonation Engine Diagnostics: Evaluation for Application in Experimental Facilities

Real-time monitoring of combustion behavior is a crucial step toward actively controlled rotating detonation engine (RDE) operation in laboratory and industrial environments. Various machine learning methods have been developed to advance diagnostic efficiencies from conventional postprocessing efforts to real-time methods. Here this work evaluates and compares conventional techniques alongside convolutional neural network (CNN) architectures trained in previous studies, including image classification, object detection, and time series classification, according to metrics affecting diagnostic feasibility, external applicability, and performance. Real-time, capable diagnostics are deployed and evaluated using an altered experimental setup. Image-based CNNs are applied to externally provided images to approximate dataset restrictions. Image classification using high-speed chemiluminescence images and time series classification using high-speed flame ionization and pressure measurements achieve classification speeds enabling real-time diagnostic capabilities, averaging laboratory-deployed diagnostic feedback rates of 4–5 Hz. Object detection achieves the most refined resolution of 20 μs in postprocessing. Image and time series classification require the additional correlation of sensor data, extending their time-step resolutions to 80 ms. Comparisons show that no single diagnostic approach outperforms its competitors across all metrics. This finding justifies the need for a machine learning portfolio containing a host of networks to address specific needs throughout the RDE research community.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1988

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

Stclair, D. C.↗
NASA NTRS1990

Embedding CLIPS in a database-oriented diagnostic system

This paper describes the integration of C Language Production Systems (CLIPS) into a powerful portable maintenance aid (PMA) system used for flightline diagnostics. The current diagnostic target of the system is the Garrett GTCP85-180L, a gas turbine engine used as an Auxiliary Power Unit (APU) on some C-130 military transport aircraft. This project is a database oriented approach to a generic diagnostic system. CLIPS is used for 'many-to-many' pattern matching within the diagnostics process. Patterns are stored in database format, and CLIPS code is generated by a 'compilation' process on the database. Multiple CLIPS rule sets and working memories (in sequence) are supported and communication between the rule sets is achieved via the export and import commands. Work is continuing on using CLIPS in other portions of the diagnostic system and in re-implementing the diagnostic system in the Ada language.

Conway, Tim↗
NASA NTRS1996

Conversion-Integration of MSFC Nonlinear Signal Diagnostic Analysis Algorithms for Realtime Execution of MSFC's MPP Prototype System

NASA's advanced propulsion system Small Scale Magnetic Disturbances/Advanced Technology Development (SSME/ATD) has been undergoing extensive flight certification and developmental testing, which involves large numbers of health monitoring measurements. To enhance engine safety and reliability, detailed analysis and evaluation of the measurement signals are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce the risk of catastrophic system failures and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. During the development of SSME, ASRI participated in the research and development of several advanced non- linear signal diagnostic methods for health monitoring and failure prediction in turbomachinery components. However, due to the intensive computational requirement associated with such advanced analysis tasks, current SSME dynamic data analysis and diagnostic evaluation is performed off-line following flight or ground test with a typical diagnostic turnaround time of one to two days. The objective of MSFC's MPP Prototype System is to eliminate such 'diagnostic lag time' by achieving signal processing and analysis in real-time. Such an on-line diagnostic system can provide sufficient lead time to initiate corrective action and also to enable efficient scheduling of inspection, maintenance and repair activities. The major objective of this project was to convert and implement a number of advanced nonlinear diagnostic DSP algorithms in a format consistent with that required for integration into the Vanderbilt Multigraph Architecture (MGA) Model Based Programming environment. This effort will allow the real-time execution of these algorithms using the MSFC MPP Prototype System. ASRI has completed the software conversion and integration of a sequence of nonlinear signal analysis techniques specified in the SOW for real-time execution on MSFC's MPP Prototype. This report documents and summarizes the results of the contract tasks; provides the complete computer source code; including all FORTRAN/C Utilities; and all other utilities/supporting software libraries that are required for operation.

Jong, Jen-Yi↗
NASA NTRS2009

Systematic Benchmarking of Diagnostic Technologies for an Electrical Power System

Automated health management is a critical functionality for complex aerospace systems. A wide variety of diagnostic algorithms have been developed to address this technical challenge. Unfortunately, the lack of support to perform large-scale V&V (verification and validation) of diagnostic technologies continues to create barriers to effective development and deployment of such algorithms for aerospace vehicles. In this paper, we describe a formal framework developed for benchmarking of diagnostic technologies. The diagnosed system is the Advanced Diagnostics and Prognostics Testbed (ADAPT), a real-world electrical power system (EPS), developed and maintained at the NASA Ames Research Center. The benchmarking approach provides a systematic, empirical basis to the testing of diagnostic software and is used to provide performance assessment for different diagnostic algorithms.

Kurtoglu, Tolga↗
NASA NTRS2010

Spontaneous Raman Scattering Diagnostics: Applications in Practical Combustion Systems

In this chapter, the recent advancements and practical aspects of laser SRS diagnostics have been reviewed wi til regards to applications in practical combustion systems. Clearly, SRS represents a theoretically and experimentally mature diagnostic technology that has become an essential tool for multiscalar measurements in turbulent combustion at elevated pressures. Today, time-, space-, spectrally, and even polarization-resolved S RS diagnostics is at hand, with aid from recent innovations in theoretical and technological developments on electro-optical or electromechanical devices. Whilst a linear increase in SRS signals can be expected in high-pressure systems (this is perhaps one of the most important advantages for using SRS in high-pressure systems), there are practical (often severe) restrictions associated with pressurized vessels, due mainly to the limited degree of optical access. This narrows ti,e available choice of diagnostics that can be employed at any given time. Point-wise SRS diagnostics provides the highest accuracy on the chemical species and temperature measurements, and will continue to remain a vital approach for the study in such harsh environments. The practical design considerations and hands-on set-up guide for SRS diagnostics provided in this chapter are rarely presented elsewhere. Although the second-harmonic Nd:YAG pulsed laser (532 nm), combined with pulse-stretching optics or the recently introduced White Cell-based laser, seems to be the most favored excitation source of choice by the research community, UV excitation will undoubtedly continue to be used on many occasions, and especially in sooting flames. Detection methods may be divided into ICCD-based nanosecond-gate detection or a rotary-chopper electromechanical shutter-based CCD array detection, and the levels of background flame emission in individual cases would determine this critical design choice. Here, a process of Raman signal calibration based on ti,e crosstalk matrix formalism was explained step-by-step. As tI,is process may be very time-consuming and expensive, a well-planned experimental approach (01' building a transferable calibration database or library (at least with in a user's own facility over a series of different testing and runs) is vitally important. Hands on advice on the design and construction of flow control systems for high pressure burner facilities were also presented.

Kojima, Jun↗
NASA NTRS2015

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Gas Turbine Engines↗
NASA NTRS2015

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Gas Turbine Engines↗
NASA NTRS2016

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Diagnosis↗
NASA NTRS2021

A Comparison of Rest-frame Ultraviolet and Optical Emission-Line Diagnostics in the Lensed Galaxy SDSS J1723+3411 at Redshift z = 1.3293

For the extremely bright lensed galaxy SDSS J1723+3411 at z = 1.3293, we analyze spatially integrated MMT, Keck, and Hubble Space Telescope spectra that fully cover the rest-frame wavelength range of 1400 ̊A to 7200 ̊A. We also analyze near-IR spectra from Gemini that cover Hαfor a portion of the lensed arc. We report fluxes for 42 detected emission lines, and upper limits for an additional22. This galaxy has extreme emission line ratios and high equivalent widths that are characteristic of extreme emission-line galaxies. We compute strong emission line diagnostics from both the rest-frame optical and rest-frame ultraviolet (UV), to constrain physical conditions and test the spectral diagnostics themselves. We tightly determine the nebular physical conditions using the most reliable diagnostics, and then compare to results from other diagnostics. We find disappointing performance from the UV–only diagnostics: they either are unable to measure the metallicity or dramatically under-estimate it; they over-estimate the pressure; and the UV diagnostic of ionization parameter has a strong metallicity dependence in this regime. Based on these results, we suggest that upcoming James Webb Space Telescope(JWST) spectroscopic surveys of galaxies in the reionization epoch should invest the additional integration time to capture the optical [O II] and [O III] emission lines, and not rely solely on the rest-frame UV emission lines. We make available the spectra; they represent one of the highest-quality emission line spectral atlases of star-forming galaxy available beyond the local universe, and will aid planning observations with JWST.

galaxies↗
DOE OSTI2013

The Status of USITER Diagnostic Port Plug Neutronics Analysis Using Attila

USITER is one of seven partner domestic agencies (DA) contributing components to the ITER project. Four diagnostic port plug packages (two equatorial ports and two upper ports) will be engineered and fabricated by Princeton Plasma Physics Lab (PPPL). Diagnostic port plugs as illustrated in Fig. 1 are large primarily stainless steel structures that serve several roles on ITER. The port plugs are the primary vacuum seal and tritium confinement barriers for the vessel. The port plugs also house several plasma diagnostic systems and other machine service equipment. Finally, each port plug must shield high energy neutrons and gamma photons from escaping and creating radiological problems in maintenance areas behind the port plugs. The optimization of the balance between adequate shielding and the need for high performance, high throughput diagnostics systems is the focus of this paper. Neutronics calculations are also needed for assessing nuclear heating and nuclear damage in the port plug and diagnostic components. Attila, the commercially available discrete-ordinates software package, is used for all diagnostic port plug neutronics analysis studies at PPPL.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

A Comprehensive Chemistry Evaluation and Diagnostics Package for E3SM – ChemDyg Version 1.1.0

The Chemistry Evaluation and Diagnostics Package (ChemDyg) is an open-source tool designed for the Energy Exascale Earth System Model (E3SM) developed by the U.S. Department of Energy. ChemDyg facilitates routine evaluation, tailored development, and in-depth analysis of atmospheric chemistry through its modular architecture, allowing users to compare model outputs with observational data. Version 1.1.0 introduces a robust set of diagnostic capabilities, including climatology, time evolution of key tracers, diurnal and annual cycle analyses, and extensive budget diagnostics. These features help identify model discrepancies and enhance the representation of atmospheric chemistry in E3SM. Each self-contained diagnostic set includes dedicated scripts and documentation for ease of use. The interactive HTML output improves data accessibility, accelerating chemistry model development. Additionally, ChemDyg's flexible framework allows for customization, enabling users to create unique diagnostic sets for specific scientific contributions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

A new visible spectroscopy diagnostic for the JET ITER-like wall main chamber

In preparation for ITER, JET has been upgraded with a new ITER-like wall (ILW), whereby the main plasma facing components, previously of carbon, have been replaced by mainly Be in the main chamber and W in the divertor. As part of the many diagnostic enhancements, a new, survey, visible spectroscopy diagnostic has been installed for the characterization of the ILW. An array of eight lines-of-sight (LOS) view radially one of the two JET neutral beam shine through areas (W coated carbon fibre composite tiles) at the inner wall. In addition, one vertical LOS views the solid W tile at the outer divertor. The light emitted from the plasma is coupled to a series of compact overview spectrometers, with overall wavelength range of 380-960 nm and to one high resolution Echelle overview spectrometer covering the wavelength range 365-720 nm. The new survey diagnostic has been absolutely calibrated in situ by means of a radiometric light source placed inside the JET vessel in front of the whole optical path and operated by remote handling. The diagnostic is operated in every JET discharge, routinely monitoring photon fluxes from intrinsic and extrinsic impurities (e.g., Be, C, W, N, and Ne), molecules (e.g., BeD, D{sub 2}, ND) and main chamber and divertor recycling (typically D{alpha}, D{beta}, and D{gamma}). The paper presents a technical description of the diagnostic and first measurements during JET discharges.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

An in situ accelerator-based diagnostic for plasma-material interactions science on magnetic fusion devices

This paper presents a novel particle accelerator-based diagnostic that nondestructively measures the evolution of material surface compositions inside magnetic fusion devices. The diagnostic's purpose is to contribute to an integrated understanding of plasma-material interactions in magnetic fusion, which is severely hindered by a dearth of in situ material surface diagnosis. The diagnostic aims to remotely generate isotopic concentration maps on a plasma shot-to-shot timescale that cover a large fraction of the plasma-facing surface inside of a magnetic fusion device without the need for vacuum breaks or physical access to the material surfaces. Our instrument uses a compact (∼1 m), high-current (∼1 milliamp) radio-frequency quadrupole accelerator to inject 0.9 MeV deuterons into the Alcator C-Mod tokamak at MIT. We control the tokamak magnetic fields – in between plasma shots – to steer the deuterons to material surfaces where the deuterons cause high-Q nuclear reactions with low-Z isotopes ∼5 μm into the material. The induced neutrons and gamma rays are measured with scintillation detectors; energy spectra analysis provides quantitative reconstruction of surface compositions. An overview of the diagnostic technique, known as accelerator-based in situ materials surveillance (AIMS), and the first AIMS diagnostic on the Alcator C-Mod tokamak is given. Experimental validation is shown to demonstrate that an optimized deuteron beam is injected into the tokamak, that low-Z isotopes such as deuterium and boron can be quantified on the material surfaces, and that magnetic steering provides access to different measurement locations. The first AIMS analysis, which measures the relative change in deuterium at a single surface location at the end of the Alcator C-Mod FY2012 plasma campaign, is also presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗