Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “accelerator modeling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Getting a THUMP from a WIMP

Producing an acceptable thermal relic abundance of dark matter with masses >> 10 2 TeV is a challenge. We propose a novel mechanism where GeV-scale states establish a tiny thermal relic abundance for dark matter, which is later promoted to ultra massive status by a very light scalar. We refer to this dark matter as a THermal Ultra Massive Particle (THUMP). Direct detection of THUMPs can be naturally expected due to large scattering cross sections mediated by low mass states that couple THUMPs to the Standard Model. Our model generically leads to signals for the associated GeV-scale states at accelerator experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Particle-in-cell Simulations of Relativistic Magnetic Reconnection with Advanced Maxwell Solver Algorithms

Abstract Relativistic magnetic reconnection is a nonideal plasma process that is a source of nonthermal particle acceleration in many high-energy astrophysical systems. Particle-in-cell (PIC) methods are commonly used for simulating reconnection from first principles. While much progress has been made in understanding the physics of reconnection, especially in 2D, the adoption of advanced algorithms and numerical techniques for efficiently modeling such systems has been limited. With the GPU-accelerated PIC code WarpX, we explore the accuracy and potential performance benefits of two advanced Maxwell solver algorithms: a nonstandard finite-difference scheme (CKC) and an ultrahigh-order pseudo-spectral method (PSATD). We find that, for the relativistic reconnection problem, CKC and PSATD qualitatively and quantitatively match the standard Yee-grid finite-difference method. CKC and PSATD both admit a time step that is 40% longer than that of Yee, resulting in a ∼40% faster time to solution for CKC, but no performance benefit for PSATD when using a current deposition scheme that satisfies Gauss’s law. Relaxing this constraint maintains accuracy and yields a 30% speedup. Unlike Yee and CKC, PSATD is numerically stable at any time step, allowing for a larger time step than with the finite-difference methods. We found that increasing the time step 2.4–3 times over the standard Yee step still yields accurate results, but it only translates to modest performance improvements over CKC, due to the current deposition scheme used with PSATD. Further optimization of this scheme will likely improve the effective performance of PSATD.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Portable, heterogeneous ensemble workflows at scale using libEnsemble

libEnsemble is a Python-based toolkit for running dynamic ensembles, developed as part of the DOE Exascale Computing Project. The toolkit utilizes a unique generator–simulator–allocator paradigm, where generators produce input for simulators, simulators evaluate those inputs, and allocators decide whether and when a simulator or generator should be called. The generator steers the ensemble based on simulation results. Generators may, for example, apply methods for numerical optimization, machine learning, or statistical calibration. libEnsemble communicates between a manager and workers. Flexibility is provided through multiple manager–worker communication substrates each of which has different benefits. These include Python’s multiprocessing, mpi4py, and TCP. Multisite ensembles are supported using Balsam or Globus Compute. We overview the unique characteristics of libEnsemble as well as current and potential interoperability with other packages in the workflow ecosystem. We highlight libEnsemble’s dynamic resource features: libEnsemble can detect system resources, such as available nodes, cores, and GPUs, and assign these in a portable way. These features allow users to specify the number of processors and GPUs required for each simulation; and resources will be automatically assigned on a wide range of systems, including Frontier, Aurora, and Perlmutter. Such ensembles can include multiple simulation types, some using GPUs and others using only CPUs, sharing nodes for maximum efficiency. We also describe the benefits of libEnsemble’s generator–simulator coupling, which easily exposes to the user the ability to cancel, and portably kill, running simulations based on models that are updated with intermediate simulation output. We demonstrate libEnsemble’s capabilities, scalability, and scientific impact via a Gaussian process surrogate training problem for the longitudinal density profile at the exit of a plasma accelerator stage. In conclusion, the study uses gpCAM for the surrogate model and employs either Wake-T or WarpX simulations, highlighting efficient use of resources that can easily extend to exascale.

Dynamic ensembles↗
DOE OSTI2021

Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations

The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parametrized from MD simulations of complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four order of magnitude. Such models go beyond the commonly employed potential energy surface fitting methods in that they are aimed purely at describing kinetic effects. So far, such kinetic models utilize molecular descriptions of reactions and have been constrained to only reproduce molecules previously observed in MD simulations. Therefore, these descriptions fail to predict the reactivity of unobserved molecules, for example in the case of large molecules or solids. In this work, we propose a new approach for the extraction of reaction mechanisms and reaction rates from MD simulations, namely the use of atomic-level features. Using the complex chemical network of hydrocarbon pyrolysis as example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parametrize them, a critical feature to describe rare events. Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events. Through comparisons of the model ability to extrapolate to longer simulation timescales and different chemical compositions than the ones used for parameterization, it is demonstrated that kinetic models employing atomic features retain the same level of accuracy and transferability as the use of features based on molecular species, while being more compact and parametrized with less data. We also find that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Adaptive time scaling for accelerating explicit finite element analysis

A method for accelerating an explicit finite element analysis (FEA) simulation of a modeled system or process includes performing an initial iteration of the FEA simulation according to a baseline time interval via an FEA computing network, and calculating a criteria ratio of a predetermined set of scaling criteria for the modeled system or process. The method includes determining a time-scaling factor using the criteria ratio via the FEA computing network as a function of the criteria ratio, and then applying the time-scaling factor to the baseline time interval to generate a scaled time interval. The scaled time interval accelerates simulation time of the FEA simulation. The method includes performing a subsequent iteration of the explicit FEA simulation at the scaled time interval using the FEA computing network. The process continues for subsequent iterations, with the time-scaling factor adapting with each iteration.

Chen, Jian↗
DOE OSTI2026

Historic climate, cosmogenic 10Be, denudation-rate, and geospatial datasets from the Pikes Peak region, Colorado, USA

This data package contains geographic information system (GIS) layers and tabular datasets associated with the study of elevation-dependent denudation rates on Pikes Peak in the Front Range of the Rocky Mountains, Colorado, USA. The package includes GIS layers used to produce the study-area map, including sample locations, sample watershed boundaries, the Pikes Peak batholith, Pleistocene glacier extent, weather station locations, and elevation and hillshade rasters, together with comma-separated value (CSV) tables and matching CSV data dictionaries. These mapped layers provide the geographic framework for interpreting denudation patterns across the Pikes Peak region and for relating sample locations to watershed geometry, bedrock setting, glacial history, and nearby climate stations. The first group of tables reports climate and geospatial context for the study area. These files include station-based temperature and precipitation data used to characterize elevational gradients in mean annual climate and monthly climate seasonality, sample locations, denudation-rate and topographic metrics, fixed frost-cracking model parameters, frost-cracking intensity and precipitation-frequency metrics, and stream-power inversion results. Together, these data provide the basis for evaluating how denudation varies with elevation, climate, and landscape form across sampled catchments on Pikes Peak. The second group of tables reports cosmogenic nuclide and erosion-model results used in the denudation analysis. Included files contain accelerator mass spectrometry (AMS) measurements for in situ-produced cosmogenic beryllium-10 (10Be), including sample identifiers, measured 10Be:9Be ratios, analytical uncertainties, carrier mass, quartz mass, blank corrections, blank-group statistics, and calculated 10Be concentrations and uncertainties. Additional tables summarize stream-power-law inversion results for sampled catchments, including optimized model parameters, predicted erosion rates, residual metrics, channel-pixel counts, and convergence status, as well as regression equations and summary statistics used to evaluate relationships among elevation, climate, frost cracking, precipitation forcing, and denudation rate. The package contains GIS files, comma-separated value files (.csv), Microsoft Excel files (.xlsx), CSV data dictionaries, a file-level metadata table, and a readme text file.

10Be cosmogenic nuclides↗
DOE OSTI2022

Projecting California Light-Duty Vehicle Attributes (2019-2035)

In this report, we estimated a set of future vehicle attribute scenarios for new light-duty vehicles for the California market using the Automotive Deployment Options Projection Tool (ADOPT). ADOPT starts simulations with all existing vehicle makes and models and endogenously creates new vehicle models over time based on assumed technology improvements and market conditions. For this study, we simulated four scenarios with varied technology, policy, and electric vehicle infrastructure assumptions. We aggregated simulation results into up to 30 vehicle classes (covering size and price classifications) for each powertrain, for six powertrains. We simulated model years 2019 to 2035. We present results for four vehicle attributes: vehicle acceleration, fuel economy (including electric and gasoline for plug-in hybrid electric vehicles [PHEVs]), vehicle range, and vehicle purchase price. We also present results showing the estimated number of vehicle models available in each vehicle class over time. In the Mid scenario, which contains conditions between our most conservative and most optimistic assumptions for emerging electric and hydrogen technologies, we observe improvements in acceleration, range (for battery-electric vehicles [BEVs]), and vehicle purchase price for electric and hydrogen powertrains. In some cases, we project that consumer preferences lead to trade-offs between vehicle attributes, such as reduced fuel economy in exchange for improved acceleration. Conventional vehicles show modest improvements in fuel economy in these scenarios due to the high numbers of BEV and PHEV sales, which reduce the improvements required in conventional vehicles to meet fleet fuel economy standards. In scenarios with advanced technology assumptions (including reduced battery price and improved energy density), we observe further improvements in BEV range and price relative to the improvements in the Mid scenario.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Millimeter observational signatures of flares in magnetically arrested black hole accretion models

In general relativistic magnetohydrodynamic (GRMHD) simulations, accreted magnetic flux on the black hole horizon episodically decays, during which magnetic reconnection heats up the plasma near the horizon, potentially powering high-energy flares like those observed in M87* and Sgr A*. We study the mm observational counterparts of such flaring episodes in very high resolution GRMHD simulations. The change in 230 GHz flux during the expected high energy flares depends primarily on the efficiency of accelerating γ ≳ 100 (T e ≳ 10 11 K) electrons. For models in which the electrons are heated to T e ∼ 10 11 K during flares, the hot plasma produced by reconnection significantly enhances 230 GHz emission and increases the size of the 230 GHz image. By contrast, for models in which the electrons are heated to higher temperatures (which we argue are better motivated), the reconnection-heated plasma is too hot to produce significant 230 GHz synchrotron emission, and the 230 GHz flux decreases during high energy flares. We do not find a significant change in the mm polarization during flares as long as the emission is Faraday thin. We also present expectations for the ring-shaped image as observed by the Event Horizon Telescope during flares, as well as multiwavelength synchrotron spectra. Our results highlight several limitations of standard post-processing prescriptions for the electron temperature in GRMHD simulations. We also discuss the implications of our results for current and future observations of flares in Sgr A*, M87*, and related systems. Appendices contain detailed convergence studies with respect to resolution and plasma magnetization.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Simulation of Impedance Changes with Aging in Lithium Titanate-based Cells Using Physics-Based Dimensionless Modeling

Quantifying aging effects in lithium-ion cells with chemistries that have a flat open circuit potential is challenging. We implement a physics-based electrochemical model to track changes in the electrochemical impedance response of lithium titanate-based cells. Frequency domain equations of a pseudo two-dimensional model are made dimensionless, and the corresponding non-dimensional parameters are estimated using a Levenberg-Marquardt routine. The model weighs the relative contributions of changes in diffusion, ionic conduction within the electrolyte phase against solid phase electronic conduction towards cell aging. Solid-phase diffusion, charge transfer resistance and double layer capacitance at the solid-liquid interface are accounted for in the particle impedance. The estimation routine tracks dimensionless parameters using accelerated cycling data from full cells over 1000 cycles. The model can be deployed within a short time for state estimation using physics-based models without requiring prior knowledge of the battery chemistry, format, or capacity.

25 ENERGY STORAGE↗
DOE OSTI2023

Simulation Study of Monoenergetic Photon Source LiDAR System Performance for Emergency Response Applications

The following report summarizes the results of a Monte Carlo simulation and modeling study of the Lawrence Berkeley National Laboratory’s (LBL) Monoenergetic Photon Source (MPS) as part of a high-energy LiDAR system for use in emergency response applications. A set of image quality indicators (IQI) and nonsensitive phantoms or generalized surrogates for search targets have been identified for use in the planned measurement campaign at the Berkeley Lab Laser Accelerator (BELLA) Center toward the end FY2023. These items were modeled and the time-of-flight (TOF) spectral responses following irradiation were calculated for a set of varying operating conditions. The goal of this study was to both predict the LiDAR system performance as well as determine if minor modifications to the IQI and phantom designs were required before fabrication. It was found that the IQI and target designs both overlap and extend towards the limit of performance of the system and are thus suitable for the final project measurement campaign.

43 - PARTICLE ACCELERATORS↗
DOE OSTI2022

Finding Electronic Structure Machine Learning Surrogates without Training

A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite modern software approaches and advances in high-performance computing. The silver lining in this regard is the use of machine learning to accelerate electronic structure calculations – this line of research has recently gained growing attention. The grand challenge therein is finding a suitable machine-learning model during a process called hyperparameter optimization. This, however, causes a massive computational overhead in addition to that of data generation. We accelerate the construction of machine-learning surrogate models by roughly two orders of magnitude by circumventing excessive training during the hyperparameter optimization phase. We demonstrate our workflow for Kohn-Sham density functional theory, the most popular computational method in materials science and chemistry.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Beam-ion Studies in NSTX and NSTX-U (Final Technical Report)

The confinement of high energy "fast ions" is crucial for the success of magnetic fusion as a practical energy source. The research performed by UC Irvine on NSTX and NSTX-U provided new information about this important topic in the configuration known as a "spherical tokamak." Injected neutral beams provided the fast ions (also known as "beam ions"). There were five overarching goals of the research. One objective was to measure the confinement of beam ions in order to ascertain if they were accomplishing their desired purpose of transferring their energy to the bulk plasma. A second goal was to better understand instabilities that are driven unstable by the fast ions. A related goal was to use the similarities and differences between NSTX and the DIII-D conventional tokamak to better understand the fast-ion driven instabilities. A fourth goal was to develop new instruments to measure fast ions and techniques that facilitate analysis of the data. The fifth objective was to measure and understand acceleration of beam ions by RF waves. Much was accomplished in all of these five areas. In the first, it was shown that the large magnetic moment of the beam ions did not harm confinement but instabilities known as the "sawtooth" and "long-lived mode" do. In the second area, much attention was devoted to the fast-ion instabilities known as "Alfvén eigenmodes." In particular, Alfvén eigenmode "avalanches" can cause ~ 30% of the fast ions to be lost in a single explosive burst. The comparative studies with DIII-D showed that an instability discovered on NSTX is also important in conventional tokamaks. In the fourth category, several instruments were developed, a number of effects that complicate interpretation of the data were understood, and progress toward a new method to infer the fast-ion distribution function from the data was made. In the fifth category, the measured profile of accelerated fast ions was initially much broader than theoretical predictions but subsequent improvements in the theoretical modeling achieved good agreement with the data.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Wildfire exacerbates high-latitude soil carbon losses from climate warming

Abstract Arctic and boreal permafrost soil organic carbon (SOC) decomposition has been slower than carbon inputs from plant growth since the last glaciation. Anthropogenic climate warming has threatened this historical trend by accelerating SOC decomposition and altering wildfire regimes. We accurately modeled observed plant biomass and carbon emissions from wildfires in Alaskan ecosystems under current climate conditions. In projections to 2300 under the RCP8.5 climate scenario, we found that warming and increased atmospheric CO 2 will result in plant biomass gains and higher litterfall. However, increased carbon losses from (a) wildfire combustion and (b) rapid SOC decomposition driven by increased deciduous litter production, root exudation, and active layer depth will lead to about 4.4 PgC of soil carbon losses from Alaska by 2300 and most (88%) of these loses will be from the top 1 m of soil. These SOC losses offset plant carbon gains, causing the ecosystem to transition to a net carbon source after 2200. Simulations excluding wildfire increases yielded about a factor of four lower SOC losses by 2300. Our results show that projected wildfire and its direct and indirect effects on plant and soil carbon may accelerate high-latitude soil carbon losses, resulting in a positive feedback to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images

Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of organismal biology, especially with the advent of large vision-language models (VLMs). We ask if pre-trained VLMs can aid scientists in answering a range of biologically relevant questions without any additional fine-tuning. In this paper, we evaluate the effectiveness of 12 state-of-the-art (SOTA) VLMs in the field of organismal biology using a novel dataset, VLM4Bio, consisting of 469K question8 answer pairs involving 30K images from three groups of organisms: fishes, birds, and butterflies, covering five biologically relevant tasks. We also explore the effects of applying prompting techniques and tests for reasoning hallucination on the performance of VLMs, shedding new light on the capabilities of current SOTA VLMs in answering biologically relevant questions using images

Maruf, M [Virginia Tech, Blacksburg]↗
DOE OSTI2020

Winter warming in Alaska accelerates lignin decomposition contributed by Proteobacteria

Background: In a warmer world, microbial decomposition of previously frozen organic carbon (C) is one of the most likely positive climate feedbacks of permafrost regions to the atmosphere. However, mechanistic understanding of microbial mediation on chemically recalcitrant C instability is limited; thus, it is crucial to identify and evaluate active decomposers of chemically recalcitrant C, which is essential for predicting C-cycle feedbacks and their relative strength of influence on climate change. Using stable isotope probing of the active layer of Arctic tundra soils after depleting soil labile C through a 975-day laboratory incubation, the identity of microbial decomposers of lignin and, their responses to warming were revealed. Results: The β - Proteobacteria genus Burkholderia accounted for 95.1% of total abundance of potential lignin decomposers. Consistently, Burkholderia isolated from our tundra soils could grow with lignin as the sole C source. A 2.2 °C increase of warming considerably increased total abundance and functional capacities of all potential lignin decomposers. In addition to Burkholderia , α - Proteobacteria capable of lignin decomposition (e.g. Bradyrhizobium and Methylobacterium genera) were stimulated by warming by 82-fold. Those community changes collectively doubled the priming effect, i.e., decomposition of existing C after fresh C input to soil. Consequently, warming aggravates soil C instability, as verified by microbially enabled climate-C modeling. Conclusions: Our findings are alarming, which demonstrate that accelerated C decomposition under warming conditions will make tundra soils a larger biospheric C source than anticipated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Probabilistic predictions and uncertainty estimation for radiological and nuclear effects modeling

Better characterization of meteorological uncertainty is needed to predict the effects of radiological and nuclear releases in the atmosphere. Multi-physics ensembles of atmospheric simulations can estimate uncertainty of physical processes, but they are often limited for computational reasons. Because weather uncertainty is important for emergency response applications, but the conventional way of running multi-physics ensembles is infeasible, we have developed new machine learning-based methods that quickly estimate and identify key sources of meteorological uncertainty. Machine learning accelerates the analysis of weather uncertainty by iteratively learning about model physics options that affect plume predictions. We have demonstrated the power of machine learning by creating a large multi-physics weather and dispersion ensemble dataset containing 1,200 simulations for two hypothetical radiological release scenarios. By training on less than 10% of the ensemble, we can quickly and accurately predict different outcomes, including the mass of radiological material deposited and spatial distribution and extent of contaminated areas.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Fostering a Guiding Multiscale Model for the Development of Advanced MgB 2 Hydrogen Storage Materials (Final Technical Report)

Project Goal and Objective. The demand for energy and for an upgraded energy infrastructure has steadily grown, as have the needs for energy independence and alternatives to our reliance on petroleum. Hydrogen is considered the most viable fuels for wide-scale implementation in the near future as it is less-polluting, non-toxic, and has more stored energy than petroleum. It is envisioned that hydrogen can eventually become the prime energy carrier, integrating the transportation, grid, and chemical sectors in a way that improves resiliency, diversifies feedstocks, and affords new economic opportunities. A key remaining challenge is the development materials with enhanced gravimetric and volumetric hydrogen storage capacities that offer a higher performance than compressed gas. These materials would eliminate the need for large-scale compression, thereby dramatically reducing the footprint and cost of gas storage. The high gravimetric and volumetric hydrogen capacities of complex hydrides has prompted an intensive investigation of the potential of this class of materials as hydrogen storage media over the past 25 years. Among the many complex hydrides that have been explored, magnesium borohydride, Mg(BH 4 ) 2 , has been found to possess the best combination of practical thermodynamic properties. These include a gravimetric H 2 density of 14.9 wt% H 2 and thermodynamics for the dehydrogenation of Mg(BH 4 ) 2 to MgB 2 (equation 1) (ΔH° = 39 kJ/mol H 2 , ΔS = 112 J/K mol H 2 ) which lie in the narrow window required Mg(BH 4 ) 2 $\Leftrightarrow$ MgB 2 + 4 H 2 (1) for reversibility under moderate pressure and temperature. However, overcoming the extremely slow kinetics of the reversible release of hydrogen by this material in the solid state is a daunting challenge. At temperatures greater than 400 °C, the borohydride releases up to 14 wt% hydrogen giving MgB 2 . We discovered that the direct re-hydrogenation of MgB 2 to Mg(BH 4 ) 2 can be accomplished under 950 bar H 2 at 400 °C. While this demonstrated that complete reversibility can be achieved, the conditions employed are far too extreme for commercial hydrogen storage applications. More recently, we found through US DOE funded research projects (EERE HyMARC and HySCOR), that hydrogen cycling, can be accomplish at much milder conditions upon modification of the borohydride or boride. Guided by these discoveries these discoveries, the objective of this research project was to obtain key information that will enable the development of a model of reversible hydrogenation of MgB 2 to Mg(BH 4 ) 2 . The ultimate goal of our efforts is to attain a model of this transformation that can be utilized to accelerate development further advanced materials. This project directly follows on discoveries that were made over the course of a US DOE, EERE HyMARC project that was focused on improvement of the hydrogen cycling kinetics of modified MgB 2 . We found that that mechanical milling with graphene results the desired, pronounced kinetic enhancement. The dramatic lowering of the conditions required for the hydrogenation of MgB 2 is a significant step towards overcoming its chemical inertness allowing its development as a practical onboard hydrogen storage material. However, the exact nature of the modification(s) of MgB 2 that is responsible for its activation towards hydrogenation is completely unknown. This situation is not unique, as efforts to develop hydrogen storage materials typically have a narrow focus rather than a comprehensive approach that takes atomic level bonding and structure; molecular dynamics; long range, nano- and mesoscale-structure and their interconnection all into account. The goal of this project was the development of a comprehensive, multi-scale computational model of reversible hydrogenation of MgB 2 to Mg(BH 4 ) 2 that can be utilized for development of higher performance versions of the modified material. Development of the model requires determination of: 1) the bulk, nano-scale, and meso-scale structural changes occurring at elevated pressure following mechano-chemical modification of MgB 2 ; 2) the reaction pathway of the reversible hydrogenation of MgB 2 to Mg(BH 4 ) 2 ; 3) the effect of elevated pressure and mechano-chemical modification on the chemical reaction pathways; 4) the interactions at solid-gas interfaces; and particle surfaces; and 5) the kinetics and thermodynamic parameters associated with each step of the hydrogenation reaction pathway. This investigation required advanced techniques as preliminary, standard XRD, 11 B NMR, and FTIR analysis showed no signs of material modification. In order to gain this level of understanding of modified MgB 2 , required the teaming of a diverse group of experts and state-of-the art experimental capabilities at the University of Hawaii at Manoa (UHM) and collaborating National Laboratories: Craig Jensen , Department of Chemistry (PI and Project Director), solid state, solution, and high pressure NMR spectroscopy; solid-state synthesis; and high pressure hydrogenation (collaboration with SNL); Godwin Severa , Hawaii Natural Energy Institute (co-PI) calorimetry; infrared and Raman spectroscopy (collaboration with NREL); Dera , high pressure X-ray diffraction including in situ experiments (collaboration with ANL); Hope Ishii , Hawaii Institute of Geophysics electron microscopy investigations (collaboration with LBNL); and Joe Brown , Mechanical Engineering , material electronic structure and electric field effects.

08 HYDROGEN↗