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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Focal Plane Alignment Utilizing Optical CMM

In many applications, an optical detector has to be located relative to mechanical reference points. One solution is to specify stringent requirements on (1) mounting the optical detector relative to the chip carrier, (2) soldering the chip carrier onto the printed circuit board (PCB), and (3) installing the PCB to the mechanical structure of the subsystem. Figure 1 shows a sketch of an optical detector mounted relative to mechanical reference with high positional accuracy. The optical detector is typically a fragile wafer that cannot be physically touched by any measurement tool. An optical coordinate measuring machine (CMM) can be used to position optical detectors relative to mechanical reference points. This approach will eliminate all requirements on positional tolerances. The only requirement is that the PCB is manufactured with oversized holes. An exaggerated sketch of this situation is shown in Figure 2. The sketch shows very loose tolerances on mounting the optical detector in the chip carrier, loose tolerance on soldering the chip carrier to the PCB, and finally large tolerance on where the mounting screws are located. The PCB is held with large screws and oversized holes. The PCB is mounted loosely so it can move freely around. The optical CMM measures the mechanical reference points. Based on these measurements, the required positions of the optical detector corners can be calculated. The optical CMM is commanded to go to the position where one detector corner is supposed to be. This is indicated with the cross-hairs in Figure 2(a). This figure is representative of the image of the optical CMM monitor. Using a suitable tapping tool, the PCB is manually tapped around until the corner of the optical detector is at the crosshairs of the optical CMM. The CMM is commanded to another corner, and the process is repeated a number of times until all corners of the optical detector are within a distance of 10 to 30 microns of the required position. The situation is sketched in Figure 2(b) (the figure also shows the tapping tool and where to tap). At this point the fasteners for the PCB are torqued slightly so the PCB can still move. The PCB location is adjusted again with the tapping tool. This process is repeated 3 to 4 times until the final torque is achieved. The oversized mounting holes are then filled with a liquid bonding agent to secure the board in position (not shown in the sketch). A 10- to 30-micron mounting accuracy has been achieved utilizing this method..

Liebe, Carl Christian↗
DOE OSTI2024

Autonomous anomaly detection of proliferation in the AGN-201 nuclear reactor digital twin

The expansion of global nuclear power necessitates advanced methods for analyzing proliferation indicators. This study introduces a novel application of the Isolation Forest Machine Learning (IFML) algorithm within a digital twin (DT) of the AGN-201 nuclear reactor to autonomously detect anomalies. Leveraging real-time operational data from the AGN-201 DT, the IFML algorithm identifies outliers without prior data labeling and operates as a lightweight, complementary approach to traditional physics-based anomaly detection methods for nuclear safeguards. In a simulated Red vs. Blue team exercise, the IFML algorithm successfully detected six significant unseen anomalies related to reactivity changes, achieving an accuracy of 99% for identifying operational deviationxs. These anomalies, caused by deliberate perturbations, were detected alongside known physics-based models, underscoring the potential of IFML to enhance real-time monitoring without displacing traditional methods. Further, this study highlights the applicability of IFML in nuclear environments by providing an additional, redundant layer of anomaly detection to improve safeguards and operational safety in complex systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1998

Laser pointing determination for the geoscience laser altimeter system

The Geoscience Laser Altimeter System (GLAS) is a space-based lidar being developed to monitor changes in the mass balance of the Earth's polar ice sheets (Thomas et al. 1985). GLAS is part of NASA's Earth Observing System (Schutz 1995), and is being designed to launch into a 600 km circular polar orbit in the year 2001, for continuous operation over 3 to 5 years. The orbit's 94 degree inclination has been selected to allow good coverage and profile patterns over the ice sheets of Greenland and Antarctica. The GLAS mission uses a small dedicated spacecraft provided by Ball Aerospace, which is required to have a very stable nadir and zenith pointing platform which points to within approximately 100 urad (20 arcseconds) of Nadir. Accurate knowledge of the laser beam's pointing angle (in the far field) is critical since pointing the laser beam away from nadir biases the altimetry measurements (Gardner 1992, Bufton et al. 1991). This error is a function of the distance of the laser centroid off nadir multiplied by the orbit altitude and the tangent of the slope angle of the terrain. Most of the ice sheet surface slopes are less than 1? resulting in pointing knowledge bias of only 7.6 cm with 7.3 urad accuracy, and overall single shot height accuracy of approximately 15 cm. However, over a 3 deg surface slope pointing knowledge to approximately 7.3 urad is the largest error source (23 cm) in achieving 26 cm height accuracy. The GLAS design incorporates a stellar reference system (SRS) to relate the laser beam pointing angle to the star field to an accuracy of 7.3 urad. The stellar reference system combines an attitude determination system (ADS) operating from 4 to 10 Hz coupled to a 40 Hz laser reference system (LRS) to perform this task.

Miller, Pamela S.↗
DOE OSTI2020

Assessment of software methods for estimating protein-protein relative binding affinities

A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83–98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1985

Sensitivity of a mesoscale model to initial specification of relative humidity, liquid water and vertical motion

The influence of synoptic scale initial conditions on the accuracy of mesoscale precipitation modeling is investigated. Attention is focused on the relative importance of the water vapor, cloud water, rain water, and vertical motion, with the analysis carried out using the Limited Area Mesoscale Prediction System (LAMPS). The fully moist primitive equation model has 15 levels and a terrain-following sigma coordinate system. A K-theory approach was implemented to model the planetary boundary layer. A total of 15 sensitivity simulations were run to investigate the effects of the synoptic initial conditions of the four atmospheric variables. The absence of synoptic cloud and rain water amounts in the initialization caused a 2 hr delay in the onset of precipitation. The delay was increased if synoptic-scale vertical motion was used instead of mesoscale values. Both the delays and a choice of a smoothed moisture field resulted in underestimations of the total rainfall.

Kalb, M. W.↗
DOE OSTI2024

Short-Term Load Forecasting Considering EV Charging Loads with Prediction Interval Evaluation

Short-term load forecasting plays a critical role in power system planning and operation. Along with the electrification of various loads, electricity demands are becoming increasingly hard to predict. Notably, the recent rise in electric vehicles (EVs) has further contributed to this unpredictability. To address this issue, this paper proposes a probabilistic load forecasting strategy utilizing Gaussian process regression, structured in a day-ahead manner. While many works focus on deterministic prediction, probabilistic forecasting offers additional insights into variability and uncertainty, enabling more flexible and reliable operation for power systems. To enhance the accuracy of the load forecasting model, the inputs include features related to EV charging habits as well as commonly used weather information. The load forecasting results are evaluated using various metrics, including conventional ones that assess the accuracy of point forecasts, as well as additional metrics that test the reliability of prediction intervals. The proposed load forecasting method is finally tested on real residential power consumption data and EV charging data sampled from real-world sources. The results prove that the new features can greatly improve the performance of the load forecasting method.

electrical vehicle↗
NASA NTRS1995

Validation of stratospheric ozone observed by the Atmospheric Trace Molecule Spectroscopy (ATMOS) experiment during 1992

Measurements of stratospheric ozone were made by the Atmospheric Trace Molecule Spectroscopy (ATMOS) experiment from onboard the Space Shuttle during the period 8-16 April 1992. The precision and accuracy of the measurements are discussed in the context of establishing the accuracy and compatibility of the measurements relative to other space-based measurements of ozone. Coincident measurements were obtained from the Stratospheric Aerosol and Gas Experiment (SAGE) 2 instrument onboard the Earth Radiation Budget Satellite (ERBS) and three instruments, the Microwave Limb Sounder (MLS), the Cryogenic Limb Array Etalon Spectrometer (CLAES), the Halogen Occultation Experiment (HALOE) onboard the Upper Atmospheric Research Satellite (UARS). Three of the instruments, ATMOS, SAGE-2 and MLS are demonstrated to be consistent to within +10% over the altitude range between 100 hPa and 0.5 hPa (approximately 20 to 60 km), with ATMOS and MLS displaying a zero mean bias and a root mean square deviation (rms) of 3% and SAGE-2 displaying an essentially systematic bias of -5% over the majority of the altitude range, except for below 30 hPa where SAGE-2 displays a large positive systematic bias due to the effect of the aerosol attenuation resulting from the Mt. Pinatubo volcanic eruption in 1991. The comparison with HALOE indicates that ozone levels observed by ATMOS are systematically higher than HALOE by 5 to 25% with a clear gradient in the differences; in contrast the comparison with CLAES displays differences of as much as + 30% with no obvious systematic scaling possible. Comparisons are evaluated in two separate schemes, by geographical coincidence and through zonal averages which illustrate the limitations of both schemes. Latitudinal and longitudinal variation are used to assess the sensitivity of the ATMOS measurements and to confirm the precision and accuracy, which should be less than 4% and 10% respectively.

Abrams, M. C.↗
DOE OSTI2023

Background studies and normalization of signal events in the Mu2e experiment

The Mu2e experiment is currently being constructed at Fermilab to search for the neutrino-less conversion of negative muons into electrons in the field of an aluminum nucleus. The experiment aims at a sensitivity of four orders of magnitude higher than previous related experiments, which implies highly demanding accuracy requirements both in the design and during the operation. To achieve such a goal, two important tasks should be accomplished. First, it is essential to estimate precisely the particle yields and all the backgrounds that could mimic the monoenergetic conversion electron signal. Second, it is necessary to normalize the signal events accurately. The normalization of the signal events is planned to be done using a detector system made of an HPGe detector and a Lanthanum Bromide detector, which will measure the rate of muons stopped on the aluminum target by looking at the emitted characteristic X-and γ-rays of energies up to 1809 keV. Therefore, it is essential to evaluate the detector system's performance before the start of the actual experiment. In this study, the first task was addressed by an extensive campaign of Monte Carlo simulations to investigate the relevant parameters and their impact on the experiment's sensitivity. The second task was handled by taking advantage of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) pulsed Bremsstrahlung photon beam at the ELBE facility. The detector system was tested at the ELBE facility under timing and background conditions similar to the ones expected at the Mu2e experiment. The study presents and discusses the simulation results and the detector system testing campaign.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1999

Direct Detection Doppler Lidar for Spaceborne Wind Measurement

The theory of double edge lidar techniques for measuring the atmospheric wind using aerosol and molecular backscatter is described. Two high spectral resolution filters with opposite slopes are located about the laser frequency for the aerosol based measurement or in the wings of the Rayleigh - Brillouin profile for the molecular measurement. This doubles the signal change per unit Doppler shift and improves the measurement accuracy by nearly a factor of 2 relative to the single edge technique. For the aerosol based measurement, the use of two high resolution edge filters reduces the effects of background, Rayleigh scattering, by as much as an order of magnitude and substantially improves the measurement accuracy. Also, we describe a method that allows the Rayleigh and aerosol components of the signal to be independently determined. A measurement accuracy of 1.2 m/s can be obtained for a signal level of 1000 detected photons which corresponds to signal levels in the boundary layer. For the molecular based measurement, we describe the use of a crossover region where the sensitivity of a molecular and aerosol-based measurement are equal. This desensitizes the molecular measurement to the effects of aerosol scattering and greatly simplifies the measurement. Simulations using a conical scanning spaceborne lidar at 355 nm give an accuracy of 2-3 m/s for altitudes of 2-15 km for a 1 km vertical resolution, a satellite altitude of 400 km, and a 200 km x 200 km spatial.

Korb, C. Laurence↗
NASA NTRS1987

Exposure time considerations in high temperature low cycle fatigue

The Conventional Strainrange Partitioning (CSRP) method for High-Temperature, Low-Cycle Fatigue (HTLCF) life prediction has its origins in the modeling of first-order, creep-fatigue waveform effects while treating as second-order effects, the influence of metallurgical or environmental time dependencies. Procedures are proposed to include the latter explicitly in the inelastic strainrange--life relations. For brevity, only the CP life relation will be presented in detail. The exposure-time effect within the CP inelastic strainrange (tensile creep reversed by compressive plasticity) was determined by tensile stresshold-time experiments for 316 SS at 816 C. Reductions in CP cyclic life of a factor of about two were observed with an increase in exposure time or a corresponding decrease in creep rate by a factor of about 100. The CP life relation has been modified to be expressed in terms of either Steady State Creep Rate (SSCR) or Exposure Time (ET). The applicability and accuracy of the time-dependent CP life relations is demonstrated by conducting verification experiments involving complex hysteresis loops. Metallographic examination revealed time-dependent degradation attributable to oxide formation and precipitation of carbides along grain boundaries.

Kalluri, S.↗
NASA NTRS1988

Exposure time considerations in high temperature low cycle fatigue

The Conventional Strainrange Partitioning (CSRP) method for High-Temperature, Low-Cycle Fatigue (HTLCF) life prediction has its origins in the modeling of first-order, creep-fatigue waveform effects while treating as second-order effects, the influence of metallurgical or environmental time dependencies. Procedures are proposed to include the latter explicitly in the inelastic strainrange-life relations. For brevity, only the CP life relation will be presented in detail. The exposure-time effect within the CP inelastic strainrange (tensile creep reversed by compressive plasticity) was determined by tensile stresshold-time experiments for 316 SS at 816 C. Reductions in CP cyclic life of a factor of about two were observed with an increase in exposure time or a corresponding decrease in creep rate by a factor of about 100. The CP life relation has been modified to be expressed in terms of either Steady State Creep Rate (SSCR) or Exposure Time (ET). The applicability and accuracy of the time-dependent CP life relations is demonstrated by conducting verification experiments involving complex hysteresis loops. Metallographic examination revealed time-dependent degradation attributable to oxide formation and precipitation of carbides along grain boundaries.

Kalluri, S.↗
DOE OSTI2021

Construction of linearly independent non-orthogonal AGP states

We show how to construct a linearly independent set of antisymmetrized geminal power (AGP) states, which allows us to rewrite our recently introduced geminal replacement models as linear combinations of non-orthogonal AGPs. Furthermore, this greatly simplifies the evaluation of matrix elements and permits us to introduce an AGP-based selective configuration interaction method, which can reach arbitrary excitation levels relative to a reference AGP, balancing accuracy and cost as we see fit.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Extended dynamic mode decomposition for model reduction in fluid dynamics simulations

High computational cost and storage/memory requirements of fluid dynamics simulations constrain their usefulness as a predictive tool. Reduced-order models (ROMs) provide a viable solution to this challenge by extracting the key underlying dynamics of a complex system directly from data. We investigate the efficacy and robustness of an extended dynamic mode decomposition (xDMD) algorithm in constructing ROMs of three-dimensional cardiovascular computations. Focusing on the ROMs' accuracy in representation and interpolation, we relate these metrics to the truncation rank of singular value decomposition, which underpins xDMD and other approaches to ROM construction. Our key innovation is to relate the truncation rank to the singular values of the original flow problem. This result establishes a priori guidelines for the xDMD deployment and its likely success as a means of data compression and reconstruction of the system's dynamics from dominant spatiotemporal structures present in the data.

Mechanics↗
DOE OSTI2025

Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning

Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress–strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts.

Winetrout, Jordan J.↗
DOE OSTI2025

Bayesian event categorization matrix approach for explosion monitoring

Current efforts to correctly categorize natural events from suspected explosion sources with data that is collected by ground- or space-based sensors presents historical challenges that remain unaddressed by the Event Categorization Matrix (ECM) model. Smaller historical events (lower yield explosions) may have data available from fewer measurement techniques than are available today, and therefore, a historical event record can lack a complete set of discriminants. The covariance structures can also differ between such observations of event (source-type) categories. Both obstacles are problematic for the classic ECM model. Our work addresses this gap and presents a Bayesian update to the previous ECM model, termed the Bayesian Event Categorization Matrix model, which can be trained on partial observations and does not rely on a pooled covariance structure. We further augment the ECM model with Bayesian Decision Theory so that false negative or false positive rates of an event categorization can be reduced in an intuitive manner. To demonstrate improved categorization rates for the Bayesian Event Categorization Matrix model, we compare an array of Bayesian and classic models with multiple performance metrics using Monte Carlo experiments. We use both synthetic and real data. Our Bayesian models show consistent gains in overall accuracy and lower false negative rates relative to the classic ECM model. Here, we propose future avenues to improve Bayesian Event Categorization Matrix models’ decision making and predictive capability.

58 GEOSCIENCES↗
DOE OSTI2021

Geometrical-Based Generative Adversarial Network to Enhance Digital Rock Image Quality

X-ray microcomputed tomography (micro-CT) is a common tool for the study of porous media structures and properties. High-quality micro-CT data are required to accurately capture pore structures. Acquiring high-quality micro-CT data, however, is not always possible, owing to application limitations and experimental constraints. Therefore, we propose a geometrical-based generative adversarial network (GAN) to rapidly restore noisy micro-CT images to their clean counterparts. The training data and related ground-truth (GT) data are scanned for 7 min and 9.5 h, respectively. To evaluate the performance of the geometrical-based GAN, a 6003 voxel image that has never been used for training is reconstructed and compared with the corresponding GT image. Histogram matching and linear normalization are implemented to adjust the histogram of the reconstructed image to that of the GT image. A watershed-based segmentation method is then applied to delineate pore and solid phases. Lastly, we measure the Minkowski functionals and petrophysical properties, including absolute permeability, pore size distribution, drainage capillary pressure-saturation curve, and imbibition relative permeability, to estimate the physical accuracy of the denoised image. The results show that the proposed geometrical-based GAN can accurately restore noisy micro-CT data. By reducing the scanning time from 9.5 h to 7 min, the expenditure of collecting micro-CT can be decreased significantly. This is particularly important for applications where time-lapse images of a dynamic process are required, high-throughput imaging is necessary for real-time data analysis, or where the quantification of large sample volumes is required.

58 GEOSCIENCES↗
DOE OSTI2021

Clustering of electromagnetic showers and particle interactions with graph neural networks in liquid argon time projection chambers

Liquid argon time projection chambers (LArTPCs) are a class of detectors that produce high resolution images of charged particles within their sensitive volume. In these images, the clustering of distinct particles into superstructures is of central importance to the current and future neutrino physics program. Electromagnetic (EM) activity typically exhibits spatially detached fragments of varying morphology and orientation that are challenging to efficiently assemble using traditional algorithms. Similarly, particles that are spatially removed from each other in the detector may originate from a common interaction. Graph neural networks (GNNs) were developed in recent years to find correlations between objects embedded in an arbitrary space. The graph particle aggregator (GrapPA) first leverages GNNs to predict the adjacency matrix of EM shower fragments and to identify the origin of showers, i.e., primary fragments. On the PILArNet public LArTPC simulation dataset, the algorithm achieves a shower clustering accuracy characterized by a mean purity of 99.4%, a mean efficiency of 99.6% and a primary identification accuracy of 99.8%. It yields a relative shower energy uncertainty of (4.1 + 1.4 / $\sqrt{\text{E(GeV)})}$% and a shower direction uncertainty of (2.1/ $\sqrt{\text{E(GeV)})}$°. Finally, the optimized algorithm is then applied to the related task of clustering particle instances into interactions and yields a mean purity of 99.8% and a mean efficiency of 99.5% for an interaction density of $\mathcal{O}(1)$ m –3 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Complete quasilinear model for the acceleration-driven lower hybrid drift instability and a computational assessment of its validity

A complete quasilinear model is derived for the electrostatic acceleration-driven lower hybrid drift instability in a uniform two-species low-beta plasma in which current is perpendicular to the background magnetic field. The model consists of coupled nonlinear velocity space diffusion equations for the volume-averaged ion and electron distribution functions. Each species' diffusion coefficient depends on a time-evolving spectral density of the electric-field energy per unit volume and a time-evolving dispersion relation. The dispersion relation is expressed analytically in integral form without the use of asymptotic limits and applies to arbitrary distribution functions, so long as they can be expressed as a function of one velocity coordinate, e.g., f⁡(vy) or f⁡(v⊥). The quasilinear model conserves energy and is complete in that it fully describes the evolution of the distribution functions, including resonant and nonresonant particle-wave interactions, while accounting for distribution-function-dependent mixed-complex frequencies. Further, the quasilinear diffusion model is solved numerically and self-consistently using a Crank-Nicolson temporal discretization and a second-order finite-volume velocity-space discretization. Numerical solutions are compared to nonlinear fourth-order accurate continuum kinetic Vlasov-Poisson simulations. Evolution of electric-field energy, growth rates, distribution functions, and diffusion coefficients are shown to be in agreement with Vlasov simulations. The quasilinear model is shown to predict anomalous transport terms, like resistivity and heating, to within a factor of order unity. Discrepancies between the quasilinear model and Vlasov simulations are assessed and attributed primarily to lack of damping in the quasilinear description and to the use of unperturbed-orbit susceptibilities in the linear theory dispersion relation. The results illuminate the predictive accuracy of the quasilinear model, place approximate bounds on its validity, and provide much needed vetting of quasilinear theory's ability to predict the nonlinear state of a microturbulent plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗