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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Time Dilated Bundt Cake Analysis of PV Output [Poster]

We present a novel method for modeling time-dependent statistics in the power signal generated by a photovoltaic (PV) system. Our white-box machine learning method is interpretable and auditable, based on principles of multiperiodic basis functions and convex optimization. Our proposed method of time dilating the daily signal to remove night time values results in a novel representation of PV power signals, evocative of a ‘Bundt cake’. The proposed model describes the marginal distribution of power output as a function of date and time. The resulting probabilistic model of a PV system can be used to perform a variety of tasks, and here, we demonstrate the application of clear sky detection.

14 SOLAR ENERGY↗
DOE OSTI2020

Accelerated version of NUBEAM capabilities in DIII-D using neural networks

A neural network model of the effects of neutral beam injection on DIII-D has been developed. The training and testing data used by the model have been generated by the NUBEAM module of TRANSP for experimental discharges from the 2018 DIII-D campaign. Using a principle component analysis to reduce the dimensionality of profile data, the model has been shown to reproduce the results of the Monte Carlo code NUBEAM with a high level of accuracy and an execution time orders of magnitude faster than the execution time of NUBEAM. Furthermore, this makes the neural network model uniquely suited to applications in model-based scenario planning (off-line) and active control (on-line), where a large number of simulation runs are required by the associated optimization tasks that need to be performed before and during the discharge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Assessing laser powder bed fusion system geometric errors through artifact-based methods

Additive manufacturing (AM) machines have developed more rapidly than standardized frameworks needed for the qualification of their geometric capabilities. While some manufacturer-specific methods exist to test capabilities and perform some calibration tasks, standardization efforts have only recently been undertaken in the form of ISO/ASTM 52902. In this study, the recommended methodology prescribed by the standard was implemented by building geometric artifacts with a laser powder bed fusion (LPBF) system and performing dimensional inspection with a coordinate measurement machine (CMM), amongst other methods. Typical dimensional capabilities of the LPBF system are identified and commentary is made on applying metrology methods, detecting geometric error, and diagnosing base causes in the LPBF system. In doing so, favored metrology practices and measurement analysis methods auxiliary to the standard are proposed. Artifact measurements were used to characterize beam positioning error and beam offset error. Methods for decoupling the effects of error sources are proposed. Difficulties in the inspection of AM components are identified, and the effects of various CMM measurement strategies are evaluated. Insights on the application of the new standard are presented, along with commentary as to its fitness for the LPBF process.

Berez, J.↗
DOE OSTI2014

Effects of an Advanced Reactor’s Design, Use of Automation, and Mission on Human Operators

The roles, functions, and tasks of the human operator in existing light water nuclear power plants (NPPs) are based on sound nuclear and human factors engineering (HFE) principles, are well defined by the plant’s conduct of operations, and have been validated by years of operating experience. However, advanced NPPs whose engineering designs differ from existing light-water reactors (LWRs) will impose changes on the roles, functions, and tasks of the human operators. The plans to increase the use of automation, reduce staffing levels, and add to the mission of these advanced NPPs will also affect the operator’s roles, functions, and tasks. We assert that these factors, which do not appear to have received a lot of attention by the design engineers of advanced NPPs relative to the attention given to conceptual design of these reactors, can have significant risk implications for the operators and overall plant safety if not mitigated appropriately. This paper presents a high-level analysis of a specific advanced NPP and how its engineered design, its plan to use greater levels of automation, and its expanded mission have risk significant implications on operator performance and overall plant safety.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Validation/Uncertainty Quantification for Large Eddy Simulations of the heat flux in the Tangentially Fired Oxy-Coal Alstom Boiler Simulation Facility

The objective of this task is to produce predictive capability with quantified uncertainty bounds for the heat flux in commercial-scale, tangentially fired, oxy-coal boilers. Validation data came from the Alstom Boiler Simulation Facility (BSF) for tangentially fired, oxy-coal operation. This task brings together experimental data collected under Alstom’s DOE project for measuring oxy-firing performance parameters in the BSF with this University of Utah project for large eddy simulation (LES) and validation/uncertainty quantification (V/UQ). The Utah work includes V/UQ with measurements in the single-burner facility where advanced strategies for O2 injection can be more easily controlled and data more easily obtained. Highlights of the work include: • Simulations of Alstom’s 15 megawatt (MW) BSF, exploring the uncertainty in thermal boundary conditions. A V/UQ analysis showed consistency between experimental results and simulation results, identifying uncertainty bounds on the quantities of interest for this system (Subtask 9.1) • A simulation study of the University of Utah’s oxy-fuel combustor (OFC) focused on heat flux (Subtask 9.2). A V/UQ analysis was used to show consistency between experimental and simulation results. • Measurement of heat flux and temperature with new optical diagnostic techniques and comparison with conventional measurements (Subtask 9.3). Various optical diagnostics systems were created to provide experimental data to the simulation team. The final configuration utilized a mid-wave infrared (MWIR) camera to measure heat flux and temperature, which was synchronized with a high-speed, visible camera to utilize two-color pyrometry to measure temperature and soot concentration. • Collection of heat flux and temperature measurements in the University of Utah’s OFC for use is subtasks 9.2 and 9.3 (Subtask 9.4). Several replicates were carried to better assess the experimental error. Experiments were specifically designed for the generation of high-fidelity data from a turbulent oxy-coal flame for the validation of oxy-coal simulation models. Experiments were also conducted on the OFC to determine heat flux profiles using advanced strategies for O2 injection. This is important when considering retrofit of advanced O2 injection in retrofit configurations.

Smith, P.J.↗
DOE OSTI2019

Scheduling Multiple Agents in a Persistent Monitoring Task Using Reachability Analysis

We consider the problem of controlling the dynamic state of each of a finite collection of targets distributed in physical space using a much smaller collection of mobile agents. Each agent can attend to no more than one target at a given time, thus agents must move between targets to control the collective state, implying that the states of each of the individual targets are only controlled intermittently. We assume that the state dynamics of each of the targets are given by a linear, time-invariant, controllable system, and develop conditions on the visiting schedules of the agents to ensure that the property of controllability is maintained in the face of the intermittent control. We then introduce constraints on the magnitude of the control input and a bounded disturbance into the target dynamics and develop a method to evaluate system performance under this scenario. Finally, we use this method to determine how the amount of time the agents spend at a given target, before switching to the next in its sequence, influences the control of the states of the entire collection of targets.

42 ENGINEERING↗
DOE OSTI2016

Evaluation of Graph Pattern Matching Workloads in Graph Analysis Systems

Graph analysis has emerged as a powerful method for data scientists to represent, integrate, query, and explore heterogeneous data sources. As a result, graph data management and mining became a popular area of research, and led to the development of plethora of systems in recent years. Unfortunately, the number of emerging graph analysis systems and the wide range of applications, coupled with a lack of apples-to-apples comparisons, make it difficult to understand the trade-offs between different systems and the graph operations for which they are designed. A fair comparison of these systems is a challenging task for the following reasons: multiple data models, non-standardized serialization formats, various query interfaces to users, and diverse environments they operate in. To address these key challenges, in this paper we present a new benchmark suite by extending the Lehigh University Benchmark (LUBM) to cover the most common capabilities of various graph analysis systems. We provide the design process of the benchmark, which generalizes the workflow for data scientists to conduct the desired graph analysis on different graph analysis systems. Equipped with this extended benchmark suite, we present performance comparison for nine subgraph pattern retrieval operations over six graph analysis systems, namely NetworkX, Neo4j, Jena, Titan, GraphX, and uRiKA. Through the proposed benchmark suite, this study reveals both quantitative and qualitative findings in (1) implications in loading data into each system; (2) challenges in describing graph patterns for each query interface; and (3) different sensitivity of each system to query selectivity. We envision that this study will pave the road for: (i) data scientists to select the suitable graph analysis systems, and (ii) data management system designers to advance graph analysis systems.

Hong, Seokyong↗
DOE OSTI2025

Characterization of Tank 9H Annulus Sample in Support of Residual Material Inventory Determinations

The Savannah River National Laboratory (SRNL) was requested by Savannah River Mission Completion (SRMC) to provide sample preparation and characterization of the Tank 9H annulus sample in support of Residual Material Inventory Determinations. One Tank 9H sample in three vials [HTF-9-25-13, HTF-9-25-14 and HTF-9-25-15], with each vial containing approximately 200 mL of the Tank 9H annulus salt solution, were delivered to the SRNL Shielded Cells for sample preparation and characterizations in February 2025. The density of the “as-received” solution contained in each of the three Tank 9H annulus sample vials were determined followed by a solid-liquid separation on each one using 0.45-micron Nalgene® nylon filter membranes. The resulting filtrates were combined to form the Tank 9H annulus sample with a total volume of about 600 mL. The combined wet solid fractions, about a total of 4.8 grams of salt material, remaining on the filter membranes were air-dried in the Shielded Cells for 72 hours. The total weight of the air-dried solids was 2.1 grams. These air-dried solids were washed with deionized water (DI water) at a phase ratio of 60 mL DI water/gram of solids to recover insoluble solids, if any. No visible or measurable quantity of insoluble solids were recovered after DI water washing of the air-dried solids because the air-dried solids completely dissolved in the DI water. The solid fraction-wash water was not combined with the 600 mL of the filtrate solution, and the resulting solution was not screened or analyzed for radionuclides. Aliquot sample volumes of the undiluted Tank 9H annulus sample were sent to the SRNL analytical services groups for radionuclides, elementals, anions and total mercury analysis by various methods including radiochemical separations/counting methods, inductively coupled plasma-atomic emission spectroscopy (ICP-AES), and Inductively Coupled Plasma Mass Spectroscopy (ICP-MS) and special preparations. All sample analyses were performed in triplicate. This report presents the analytical characterization results for the Tank 9H annulus sample. The results are also reported where analytical methods yielded additional analytes, other than those requested by SRMC. In the characterization of the Tank 9H annulus sample, the detection limits for all the analytes, as specified in the Technical Task Request (TTR) and Task Technical and Quality Assurance Plan (TTQAP), were met.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Region templates: Data representation and management for high-throughput image analysis

Compartment fires exhibit unique features associated with smoke accumulation and restricted air ventilation. The objective of the present study is to evaluate the ability of CFD-based fire models to simulate these features and in particular the effects of flame extinction that results from under-ventilated fire conditions. The study is performed using FireFOAM; FireFOAM is an advanced fire modeling software developed by FM Global and is based on a general-purpose open-source software called OpenFOAM. A new flame extinction model based on the concept of a critical value of the flame Damköhler number is incorporated into FireFOAM. The performance of the extinction model is evaluated via comparisons with a previously developed experimental database corresponding to a reduced-scale, heptane-fueled, compartment fire configuration. The numerical simulations also include a description of fuel evaporation driven by the computed gas-to-liquid heat feedback. Comparisons between experimental data and numerical results provide a suitable test bed to evaluate the ability of CFD-based fire models to describe the transition from over- to under-ventilated fire conditions, as well as the transition from extinction-free conditions to conditions in which the flame experiences partial or total quenching.

Energy & Fuels↗
DOE OSTI2022

OpenMP Target Task: Tasking and Target Offloading on Heterogeneous Systems

This work evaluated the use of OpenMP tasking with target GPU offloading as a potential solution for programming productivity and performance on heterogeneous systems. Also, it is proposed a new OpenMP specification to make the implementation of heterogeneous codes simpler by using OpenMP target task, which integrates both OpenMP tasking and target GPU offloading in a single OpenMP pragma. As a test case, the authors used one of the most popular and widely used Basic Linear Algebra Subprogram Level-3 routines: triangular solver (TRSM). To benefit from the heterogeneity of the current high-performance computing systems, the authors propose a different parallelization of the algorithm by using a nonuniform decomposition of the problem. This work used target GPU offloading inside OpenMP tasks to address the heterogeneity found in the hardware. This new approach can outperform the state-of-the-art algorithms, which use a uniform decomposition of the data, on both the CPU-only and hybrid CPU-GPU systems, reaching speedups of up to one order of magnitude. The performance that this approach achieves is faster than the IBM ESSL math library on CPU and competitive relative to a highly optimized heterogeneous CUDA version. One node of Oak Ridge National Laboratory’s supercomputer, Summit, was used for performance analysis.

Valero Lara, Pedro↗
DOE OSTI2022

Bridging Equipment Reliability Data and Robust Decisions in a Plant Operation Context

In order to reduce operation and maintenance (O&M) costs, nuclear power plants (NPPs) are moving from corrective and periodic maintenance to predictive maintenance strategies. Such transition requires changes on the data that needs to be retrieved and on the type of decision processes to be employed. Advanced monitoring and data analysis technologies are essential to support predictive strategies. They can in fact provide precise information about health of a component, track its degradation trends, and provide information of its expected failure time. With such information, maintenance operations for a component can be performed right before its expected failure time. This dynamic context of O&M operations requires new methods to analyze data, propagate component health information from the component to the system level, and optimize plant resources. In this respect, the risk informed asset management (RIAM) project has been tasked to develop and test this new class of methods into a risk analytics toolset. This toolset consists of data analytics tools coupled with reliability methods designed to manage plant assets and performances in a predictive maintenance context. This report shows the latest improvements on such development and the initial testing of our methods on the three main research areas that the RIAM project is focusing on. These areas are the following: equipment reliability data analytics, system reliability modeling, and plant resources optimization methods. We show how the methods developed in these areas can support predictive maintenance strategies by: 1) analyzing equipment reliability data (either in numeric and textual form), 2) assessing component and system health through an innovative margin-based reliability approach, and 3) identifying the most critical components and set optimal maintenance schedule based on plant economic and operational constraints.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

DEEP CELLULAR RECURRENT NEURAL ARCHITECTURE FOR EFFICIENT MULTIDIMENSIONAL TIME-SERIES DATA PROCESSING

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the?art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units.

Vidyaratne, Lasitha S.↗
DOE OSTI2016

SU-F-T-305: Clinical Effects of Dosimetric Leaf Gap (DLG) Values Between Matched Varian Truebeam (TB) Linacs

Purpose: The dosimetric leaf gap (DLG) is an important parameter to be measured for dynamic beam delivery of modern linacs, like the Varian Truebeam (TB). The clinical effects of DLG-values on IMRT and/or VMAT commissioning of two “matched” TB linacs will be presented.Methods and Materials: The DLG values on two TB linacs were measured for all energy modalities (filtered and FFF-modes) as part of the dynamic delivery mode commissioning (IMRT and/or VMAT. After the standard beam data was modeled in eclipse treatment planning system (TPS) and validated, IMRT validation was performed based on TG1191 benchmark, IROC Head-Neck (H&N) phantom and sample of clinical cases, all measured on both linacs. Although there was a single-set of data entered in the TPS, a noticeable difference was observed for the DLG-values between the linacs. The TG119, IROC phantom and selected patient plans were furnished with DLG-values of TB1 for both linacs and the delivery was performed on both TB linacs for comparison. Results: The DLG values of TB1 was first used for both linacs to perform the testing comparisons. The QA comparison of TG119 plans revealed a great dependence of the results to the DLG-values used for the linac for all energy modalities studied, especially when moving from 3%/3mm to 2%/2mm γ-analysis. Conclusion: The DLG-values have a definite influence on the dynamic dose, delivery that increases with the plan complexity. We recommend that the measured DLG-values are assigned to each of the “matched” linacs, even if a single set of beam data describes multiple linacs. The user should perform a detail test of the dynamic delivery of each linac based on end-to-end benchmark suites like TG119 and IROC phantoms.1Ezzel G., et al., “IMRT commissioning: Multiple institution planning and dosimetry comparisons, a report from AAPM Task Group 119.” Med. Phys. 36:5359–5373 (2009). partly supported by CAMC Cancer Center and Alliance Oncology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Analyzing the Performance Trade-Off in Implementing User-Level Threads

User-level threads have been widely adopted as a means of achieving lightweight concurrent execution without the costs of OS-level threads. Nevertheless, the costs of managing user-level threads represent a performance barrier that dictates how fine grained the concurrency exposed by an application can be without incurring significant overheads; this in turn may translate into insufficient parallelism to exploit highly parallel systems. This article is a deep dive into the fundamental costs in implementing user-level threads. We first identify that one of the highest sources of fork-join overheads stems from deviations, events that incur context switching during the execution of a thread and disrupt a run-to-completion execution. We then conduct an in-depth investigation of a wide spectrum of methods with respect to how they handle deviations while covering both parent- and child-first scheduling policies. Our methodology involves a comprehensive instruction- and cache-level analysis of all methods on several modern CPU architectures. Finally, the primary finding of our evaluation is that dynamic promotion methods that assume the absence of deviation and dynamically provide context-switching support offer the best trade-off between performance and capability when the likelihood of deviation is low.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

An initial exploration of Bayesian model calibration for estimating the composition of rocks and soils on Mars

The Mars Curiosity rover carries an instrument, ChemCam, designed to measure the composition of surface rocks and soil using laser-induced breakdown spectroscopy (LIBS). The measured spectra from this instrument must be analyzed to identify the component elements in the target sample, as well as their relative proportions. This process, which we call disaggregation, is complicated by so-called matrix effects, which describe nonlinear changes in the relative heights of emission lines as an unknown function of composition due to atomic interactions within the LIBS plasma. In this work, we explore the use of the plasma physics code ATOMIC, developed at Los Alamos National Laboratory, for the disaggregation task. ATOMIC has recently been used to model LIBS spectra and can robustly reproduce matrix effects from first principles. The ability of ATOMIC to predict LIBS spectra presents an exciting opportunity to perform disaggregation in a manner not yet tried in the LIBS community, namely via Bayesian model calibration. However, using it directly to solve our inverse problem is computationally intractable due to the large parameter space and the computation time required to produce a single output. Therefore, we also explore the use of emulators as a fast solution for this analysis. We discuss a proof of concept Gaussian process emulator for disaggregating two-element compounds of sodium and copper. The training and test datasets were simulated with ATOMIC using a Latin hypercube design. After testing the performance of the emulator, we successfully recover the composition of 25 test spectra with Bayesian model calibration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Sister Rod Destructive Test Results (FY19)

This report documents work performed under the Spent Fuel and Waste Disposition, Spent Fuel and Waste Science and Technology program for the US Department of Energy (DOE) Office of Nuclear Energy (NE). This work was performed to fulfill Level 2 Milestone M2SF-19ORO010201026 within work package SF-19OR01020102 and is an update to the work reported in M2SF-19OR010201028 and M3SF-19OR010201027. The document incorporates comments received with respect to the previous reports and incorporates additional test results. As a part of the US Department of Energy (DOE) Office of Nuclear Energy (NE) High Burnup Spent Fuel Data Project, Oak Ridge National Laboratory (ORNL) is performing destructive examinations (DEs) of high burnup (HBU) (>45 gigawatt days per metric ton uranium) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called the “sister rods,” are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4 (Zirc-4), low-tin Zircaloy-4 (LT Zirc-4), ZIRLO ® , and M5 ® . The DEs are being conducted to obtain a baseline of the HBU rod’s condition prior to dry storage and are focused on understanding overall SNF rod strength and durability. Both composite fuel and empty cladding will be tested to derive material properties. While the data generated can be used for multiple purposes, a primary goal for obtaining the post-irradiation examination data and the associated measured mechanical properties is to support SNF dry storage licensing and relicensing activities by (1) addressing identified knowledge gaps and (2) enhancing the technical basis for post-storage transportation, handling, and consolidation activities. This report documents the status of the ORNL Phase I DEs of 8 sister rods and outlines the DE tasks performed and the data collected to date, as guided by the sister rod test plans. The DEs are performed using a phased approach, and the Phase 1 DEs being performed at ORNL include: • full-length rod heat treatments (FHT) of 3 selected sister rods to examine the effects of temperatures reached during dry storage preparation, • rod internal pressure and void volume measurements of the 3 FHT rods and 3 corresponding baseline rods, as well as 2 additional rods selected for depressurization/gas transmission tests and fatigue lifetime tests, • fission gas sampling and analysis, • depressurization and gas transmission tests, • rough segmentation of the selected rods for mechanical tests and rod characterization, • fuel sampling and burnup analysis, • metallography, • cladding total hydrogen measurements, • mechanical testing, including fatigue lifetime (CIRFT), four-point bend, axial tension, microhardness, ring compression, and burst tests. Table S-1 provides a summary of the DE status. The mechanical testing will be performed using fueled segments and is expected to complement previous and current mechanical test results using defueled cladding segments.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

USU Alternative and Unconventional Energy Research and Development

The purpose and rationale of this project has been to develop enduring research capabilities at Utah State University (USU) and the Utah State University Research Foundation (USURF) in a number of energy efficient and renewable energy areas including primarily a) algae energy systems, b) solar lighting, c) intuitive buildings, d) electric transportation, 3) unconventional energy environmental monitoring and beneficial reuse technologies (water and CO 2 ), f) wind energy profiling, and g) land use impacts. The long-term goal of this initiative has been to create high-wage jobs in Utah and a platform for sustained faculty and student engagement in energy research. The program’s objective has been to provide a balanced portfolio of R&D conducted by faculty, students, and permanent staff. This objective has been met. While some of the project’s tasks met with more success than others, as with any research project of this scope, overall the research has contributed valuable technical insight and broader understanding in key energy related areas. The algae energy systems research resulted in a highly productive workforce development enterprise as it graduated a large number of well prepared students entering alternative energy development fields and scholarship. Moreover, research in this area has demonstrated both the technological and economic limitations and tremendous potential of algae feedstock-based energy and co-products. Research conducted in electric transportation, specifically in both stationary and dynamic wireless inductive coupling charging technologies, has resulted in impactful advances. The project initiated the annual Conference on Electric Roads and Vehicles (http://www.cervconference.org/), which is growing and attracts more than 100 industry experts and scholars. As a direct result of the research, the USU/USURF spin-out startup, WAVE (Wireless Advanced Vehicle Electrification), continues work in wirelessly charged bus transit systems and is poised to quickly become a multi-million dollar company with clients around the globe. Moreover, USU students and researchers alike are now on the leading edge of the electrified transportation workforce. Finally, the legacy of this DOE investment in electric transportation is continuing at USU with the formation in progress of an industry sponsored research center built around the Electric Roadway and Vehicle (EVR) research facility and test track (http://evr.usu.edu). The research conducted in unconventional energy environmental monitoring and beneficial reuse technologies experienced broad success developing experimental and modeling tools and implementing those tools to better understand environmental impacts of industrial processes used in unconventional energy development in the Utah’s Uintah Basin. Before this project began the USU Uintah Basin branch campus had minimal capability to perform this regionally critical environmental research. This research investment enabled monitoring and modeling equipment and expertise to assess impacts of energy development to all aspects of environmental quality. Laboratory capability for environmental analysis has been developed and engaged along with field testing at multiple locations. Successful campaigns to measure greenhouse gas and hydrocarbon emissions from produced water surface impoundments and leakage from subsurface oil and gas infrastructure were executed. A computer model of meteorological conditions during winter inversion episodes was created, and commercialization efforts are underway for those models. Finally, in the past 24 months, nearly $2 million in non- DOE external funding from state, local, federal and private entities has been awarded to USU Uintah Basin to continue and add to work and capability established by this task. The preceding examples represent a few highlights resulting from the USU Alternative and Unconventional Energy Research and Development project. The following report provides further detail.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗