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The lab of applied acoustics described focuses on development of instrumentation, methods and sensors with a focus on difficult and challenging conditions (high pressure, high temperature, corrosive media, radiation, etc.).
This report documents work completed in FY21 under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program’s Molten Salt Reactor (MSR) Application Drivers activity at Argonne. The common focus was on identifying the modeling and simulation functional requirements for designing, licensing, and operating MSRs and applying those capabilities already developed in NEAMS tools to example problems of interest. The four main parts of this report each focuses on a specific area of simulation physics as it relates to MSR phenomena: fuel evolution, chemistry, computational fluid dynamics (CFD), and systems analysis. In terms of fuel evolution, which includes depletion, decay, on-line separations, and transmutation, the current state of computational capabilities for modeling this behavior in liquid-fueled molten salt reactor is discussed. Some of the functional requirements to accomplish the various applications of MSR fuel depletion modeling are highlighted, followed by a summary of recent approaches and code development activities. The chemistry functional requirements were discussed in the context of several applications of high importance for MSRs, such as corrosion, salt chemistry, and species transport. Each of these types of chemistry modeling have considerable impact on various aspects of reactor applications, including informing on reactor designs, improving operational efficiencies, analyzing safety and reactivity concerns, and estimating the mechanistic source term of the reactor. A brief overview is also provided on code development activities ongoing under NEAMS relevant to chemistry modeling of MSRs. In terms of CFD applications, the state-of-the-art spectral element code, Nek5000 was used to model the fluid dynamics within a full core of the Molten Salt Fast Reactor (MSFR) concept designed as part of the Euratom EVOL project. This concept was selected as the challenge problem because of its similar features to several U.S. industry concepts. The goal was to model some of the fast MSR design challenges, including potential large internal re-circulations, the need of accurate tracking of delayed neutron precursors (DNP), and the lack of relevant thermal-hydraulics models/correlations, etc. Therefore, a series of CFD models were created for the MSFR core cavity using a k – τ model two-equation model for the turbulence. These first full core results demonstrated that any potential recirculation zones could be properly identified with the current NEAMS CFD capabilities. The development of these models will also set the stage for future testing of Nek5000’s functionalities to model other MSR thermal-fluid phenomena. Lastly, the validation of SAM against experimental data from the Molten Salt Reactor Experiment, which started in FY19, continues with the inclusion of modeling the reactivity insertion tests. This involved using SAM and its point kinetics model for flowing fuel salt to recreate the time dependent power changes and response after positive reactivity insertions at the 1, 5, and 8 MWt power levels. Through these exercises, several code modifications were suggested to the SAM development team and accommodated to enable closer agreement with solid technical and physical justifications. These include adding a moderator reactivity feedback coefficient as an available input and modifying the solution approach for the point kinetics model. Additional SAM development suggestions for flowing fuel MSRs include adding the capability to allow the moderator power change proportionally with the reactor power and enabling specification of the power and DNP distributions separately.
In the field of energy storage, electrolytes are the materials or liquids used to transport ions or redox active chemicals to facilitate an electrochemical process. Conventional electrolytes are the most common form of electrolyte used and consist of salts dissolved in a liquid solvent medium. While conventional electrolytes are generally inexpensive and work well with lithium-ion batteries, they have many shortfalls however, such as corrosion of the cell, flammability, toxicity, price, and incompatibility with other metals. This project investigates the fundamental properties in nonconventional electrolyte systems with an initial focus on eutectics being made. Using electrochemical methods ( Cyclic Voltammetry (CV), Impedance, Conductance, etc.), we are studying the study of electrolyte systems containing ions used for thermal, chemical, or electrochemical energy storage such as Li + , Mg 2+ , Na + , K + , Al 3+ , CO 3 2- , Cl - , and O 2- . This research looks at how ion mobility effects the electrochemical characteristics and properties in green energy applications.
The Electron Ion Collider (EIC) accelerator complex includes multiple high-energy high-power accelerators [1]. Operation of such a complex is associated with numerous dangers of possible damage to the equipment. The EIC machine protection system (MPS) scope is to protect the components of each accelerator from damage by the beam. The EIC MPS design, commissioning and operation is closely interconnected with the protection of various accelerator subsystems, such as RF-system, cryogenics, vacuum system etc. Yet, the primary goal of the MPS is to protect the EIC accelerators from the possible damage caused by electron and proton beams.
The Electron Ion Collider (EIC) accelerator complex includes multiple high-energy high-power accelerators [1]. Operation of such a complex is associated with numerous hazards and risks of possible damage to the equipment. The EIC machine protection system (MPS) scope is to protect the components of each accelerator from damage by the beam. The EIC MPS design, commissioning and operation is closely interconnected with the protection of various accelerator subsystems, such as RF-system, cryogenics, vacuum system etc. Yet, the primary goal of the MPS is to protect the EIC accelerators from the possible damage caused by electron and proton beams.
Thermoset polymers (e.g. epoxies, vulcanizable rubbers, polyurethanes, etc.) are crosslinked materials with excellent thermal, chemical, and mechanical stability; these properties make thermoset materials attractive for use in harsh applications and environments. Unfortunately, material robustness means that these materials persist in the environment with very slow degradation over long periods of time. Balancing the benefits of material performance with sustainability is a challenge in need of novel solutions. Here, we aimed to address this challenge by incorporating boronic acid-amine complexes into epoxy thermoset chemistries, facilitating degradation of the material under pH neutral to alkaline conditions; in this scenario, water acts as an initiator to remove boron species, creating a porous structure with an enhanced surface area that makes the material more amenable to environmental degradation. Furthermore, the expulsion of the boron leaves the residual pores rich in amines which can be exploited for CO 2 absorption or other functionalization. We demonstrated the formation of novel boron species from neat mixing of amine compounds with boric acid, including one complex that appears highly stable under nitrogen atmosphere up to 600 °C. While degradation of the materials under static, alkaline conditions (our “trigger”) was inconclusive at the time of this writing, dynamic conditions appeared more promising. Additionally, we showed that increasing boronic acid content created materials more resistant to thermal degradation, thus improving performance under typical high temperature use conditions.
Kier + Wright, as Qualified SWPPP Developer (QSD), puts forth this Storm Water Pollution Prevention Plan (SWPPP) for the Limited Area, Multi-Purpose (LAMP) High Bay Laboratory facility (Project) located at Sandia National Laboratories, 7011 East Avenue, CA. The property is owned by the U.S. Department of Energy, and managed and operated by National Technology & Engineering Solutions of Sandia, LLC. The project proposes converting an asphalt parking lot into a new high bay machine shop building and a low bay office building. Per the California State Water Resources Control Board’s (California State Water Board) Construction General Permit (CGP), a SWPPP is required when 1 acre or more of land is disturbed. The project site area of 1.6 acres exceeds the minimum acreage threshold of 1 acre and therefore requires SWPPP implementation. QSD has determined the sediment risk for this project, based on soil type at the site and starting and ending dates of construction, to be low (Section 3.4.1 and Appendix B). Receiving water for this project is the Arroyo Seco. QSD has determined the Arroyo Seco to be a high-risk receiving water because it has the three beneficial uses of “spawn”, “cold”, and “migratory” (Sections 3.3 and 3.4.2 and Appendix B). QSD has determined the overall risk level for the site to be Risk Level 2, based on a combination of low sediment risk and high receiving water risk (Appendix B). As such, QSD has delineated a variety of Best Management Practices (BMPs) to be employed during project construction to reduce or eliminate pollutants in stormwater runoff or any other discharges from the Project site. In addition to site-specific BMPs, this SWPPP report provides instruction for on site monitoring. Electronic copies of required documentation such as inspection reports, REAPs, annual report documentation, etc. shall be submitted to NTESS Sandia Delegated Representative via Newforma.
Los Alamos National Laboratory has recently obtained a Spot robot from Boston Dynamics to evaluate its suitability for work in and around laboratory facilities. Spot is a highly mobile quadruped robot capable of up to 90 minutes of operation. It can be outfitted with payloads up to 35 lbs. and supports both Boston Dynamics developed equipment, third party payloads, or custom developed payloads. One existing Boston Dynamics attachment is a robot arm that allows the robot to directly interact with its environment, open doors, turn valves, etc.
The diverse portfolio of biomass sources that is available in the Southeastern U.S., including a significant supply of pine “residue”, represents a valuable strategic position for the region. Through blends formulated based on critical properties, this project will take full advantage of the range in biomass properties afforded by the portfolio to produce a consistent, high-performance feedstock for the industry, while lowering cost. Key developments being targeted to enable this potential include whole-tree transport to a state-of-the-art merchandising depot that will further access biomass from ongoing, forest industry operations. The approach will more effectively utilize the tree and distribute cost, while minimizing in-woods contamination of the woody biomass component. To implement this vision, information on the chemical composition and changes that are induced during multiple preprocessing steps (size reduction, moisture removal, densification, etc.) is needed. New NIR sensor technology will be developed for online monitoring of important biomass properties. The data will be incorporated into a statistical process control platform to improve process efficiency and meet required specifications. Advanced process models are being developed to inform the techno-economic and life-cycle assessment of the program’s impact. The new system will ultimately reduce operational risks from supply chain disruptions, and allow operation of larger-scale biorefineries.
DOE O 435.1 and DOE M 435.1-1 require that a determination of continued adequacy of the performance assessment (PA) (CP-60089), composite analysis, and disposal authorization statement (DAS) be made annually, and these guidelines must be used to consider the results of data collection and analysis from research, field studies, and monitoring as well as provide the need to update any radioactive waste management basis documents. Beginning in 1996, the Environmental Restoration Disposal Facility (ERDF) started accepting low-level radioactive, hazardous, and mixed wastes generated during cleanup activities at the Hanford Site. ERDF is composed of a series of cells or disposal areas and can accommodate future design expansions as needed. Currently, there are 10 cells. During this reporting period (fiscal year 2020, which extended from October 1, 2019, through September 30, 2020), approximately 3.39E+04 U.S. tons (3.07E+04 metric tons) of waste was disposed at ERDF. From ERDF inception through September 30, 2020, approximately 18.5 million U.S. tons of waste has been disposed at ERDF, which equates to the consumption of approximately 88% of the disposal volume. As a condition of the DAS, disposal operations within ERDF must be in accordance with the waste acceptance criteria (ERDF-00011) that provide specific radionuclide disposal limits, waste form restrictions, and descriptions of acceptable waste packages in compliance with DOE M 435.1-1 requirements. The ERDF waste acceptance criteria stipulate that waste destined for disposal at ERDF be controlled based on source, physical form, and contaminant concentration and activity levels. There have been no changes to the physical configuration of ERDF or to the waste forms (source, physical form, etc.). No new Unreviewed Disposal Question Screenings or Evaluations have been generated during this reporting period. Therefore, there are no noted impacts to the PA, composite analysis, DAS, or radioactive waste management basis documents resulting from the evaluations and screenings. Sum of fraction analysis shows that the disposed inventory meets both the concentration and inventory threshold requirements. A sum of fractions value is computed for ERDF sensitive radionuclides contributing to the all pathways and air pathway inventory limits. Computed values were 8.85E-02 and 1.78E-01, respectively. The disposed waste inventory remained well under the PA imposed limits. Required monitoring was satisfactorily completed during the fiscal year reporting period (fiscal year 2020). Compliance with performance objectives were met as each of the reported values were well below the established limit. Overall, there are no substantive changes to primary PA assumptions nor changes to the PA analysis conclusion; therefore, compliance with DOE O 435.1 and the DAS is maintained.
This literature review explores the options of using photoluminescence for tagging uranium fuel and zirconium cladding in order to easily and quickly associate interdicted material to its facility of origin or pathway after loss of institutional control. Photoluminescence is the spontaneous emission of radiation by a substance when excited by interaction with light. The source of photoluminescence can be from organic, inorganic, and organometallic compounds. Each luminophore, or compound responsible for luminescent properties, has individual strengths and weaknesses, depending on the intended use. Many factors can affect the brightness, lifetime, color, and detectability of luminophores, such as humidity, temperature, oxygen availability, noise from radiation–induced luminescence, and the presence of quenching substances like iron. Application of luminescent compound to the surface of an item can be done using paint, dye, thin film, thick film, or electrochemical coating. Several of the application techniques may have secondary functions such as corrosion resistance and detection, heat resistance, protective properties, and more. When considering cladding, there are various successful studies on the application of luminescent coatings on zirconium. However, significant research will be necessary to explore which luminophore and which application technique would best fit the intended use. In order to proceed with this research, a baseline of the natural luminescence of the intended base material (nuclear fuel or cladding) is necessary. While there may be issues with background noise, quenching from the environment, quenching from the substrate, signal to noise weakness, etc. the multitude of options may make photoluminescent tagging an option. In summary, this review has demonstrated that photoluminescence is a potential option to tag nuclear fuel and cladding, but additional paper–studies, testing, and research is required to understand the compatibility of luminescent tags in nuclear reactor environments, manufacturability, and detectability.
NSRC’s lineages dates to the Technical Library formed by J. Robert Oppenheimer in 1943 as part of the Manhattan Project. Houses 75+ years of nuclear weapons research, designs, procedures, reports, etc. This is the largest collection of nuclear weapons information anywhere in the nation.
The technologies required to advance society will come from research from a broad range of scientific disciplines including materials science, chemistry, physics, and biology where the innovation relies on a scientific breakthrough rather than a software or business model innovation. This science-based technology focus can be referred to as “hardtech,” although there is much crossover with the concept of “cleantech.” These new technology developments will have vast reach across most industrial sectors - energy, materials, manufacturing, electronics, life sciences, etc. - with great economic consequences. The majority of these scientific breakthroughs will be produced and developed by those companies best suited for high-risk, early-stage development work: startups.
Public bus transit services in the U.S. are responsible for at least 19.7 million metric tons of CO 2 emission annually. Electric vehicles (EVs) can have a much lower environmental impact than comparable internal combustion engine vehicles (ICEVs), especially in urban areas. Unfortunately, EVs are also much more expensive than ICEVs. As a result, many public transit agencies can afford only mixed fleets of transit vehicles, consisting of EVs, hybrids (HEVs), and ICEVs. Transit agencies that operate such mixed fleets of vehicles face a challenging optimization problem: these agencies need to decide which vehicles are assigned to serving which transit trips. Since the advantage of EVs over ICEVs varies depending on the route and time of day (e.g., the benefit of EVs is higher in slower traffic with frequent stops and lower on highways), the assignment can have a significant effect on energy use and, hence, environmental impact. Through this project, we have developed reference data about energy collections and constructed a set of machine learning models that can accurately predict the energy consumption for the whole fleet at the level of each trip. We have used these models to develop a scheduling and assignment strategy that can rotate the different vehicle types across the transit agencies’ routes. The optimization algorithm ensures that the vehicles are matched to trips considering weather patterns, expected congestion, and road gradients to minimize the overall energy usage. We list the key observations from our project for other practitioners below. Details are available in the report, and the list of source code and our publications are included in the appendix. 1. We have demonstrated the feasibility of collecting, merging and analyzing large volumes of high-resolution real-world telemetry data from a mixed vehicle fleet. To mitigate the inherent noise of the recorded GPS points, the team developed an algorithm that filters data and maps the points onto a street. The algorithm considers previous and subsequent location measurements and different characteristics of nearby streets to determine how likely the vehicle travels on them. Then, the team segmented the time series into disjoint contiguous samples based on adjacent road segments and repeated the outlier detection and removal. For each data point, the team added features corresponding to elevation changes within the samples, weather features, such as temperature, and traffic data, such as speed ratio between actual speed and free-flow speed. 2. We have developed two forms of machine learning models that be used to understand and analyze the energy operations of a mixed vehicle transit fleet. The micro prediction model provides estimates of instantaneous energy prediction for all types of buses (diesel, hybrid, and electric). Such a model is important in evaluating the energy impacts of real-time bus operation strategies, but it is challenging due to diversified driving cycles of transit buses. The model can help the drivers understand the impact of their driving behaviors and short-term congestions. The macro prediction models estimate average energy consumption across the whole trip considering the features: distance traveled, various road-type features, elevation change, day of the week, time of day, various weather features (temperature, humidity, etc.), and traffic features (speed ratio and jam factor). 3. We have demonstrated that it is possible to transfer the machine learning models we have developed in this project to other teams and cities by using inductive transfer learning. We also showed that the performance of the macro energy prediction models can be improved using a multi-task learning approach where the learning parameters are shared between the models being developed for different vehicle types. The advantage of this approach is improved learning performance as the models can exploit common spatio-temporal and environmental characteristics. 4. Finally, we have developed trip and vehicle assignment and scheduling algorithms that use the energy prediction models and develop a trip to vehicle type (diesel, electric, hybrid) assignment for the whole operation to reduce overall emissions and cost. We have shown through simulations that the proposed algorithms can save $\$$ 48,910 in energy costs and 175 metric tons of CO 2 emission annually for CARTA.
Recent advancements in carbon nanotube (CNT) research have enabled lightweight, conductive wires as a transformative technology. Metal-carbon nanotube (CNT) hybrid conductors aim to combine the high conductivity of traditional metals with the low mass and temperature coefficient of resistance (TCR) of carbon nanotubes. The high conductivity of copper makes it a promising candidate to combine with CNTs in a hybrid structure, but there is limited physical and electrical interaction between copper and CNTs. The use of an interfacial layer offers one method of improving the interconnection of a Cu-CNT hybrid conductor. Over the course of this grant, a joule heating-driven chemical vapor deposition (CVD) technique was developed to deposit nanometal seeds throughout a porous, low-density (0.12 g/cm 3 , ~10 tex or mg/m) CNT roving template. Modification of the applied current to the CNT roving allows for the tuning of depositions towards either hot-spot site-specificity or overall uniformity. The effects of temperature, pressure, precursor mass, and the interval of applied current were investigated, demonstrating nanometal depositions ranging from less than 5 % w/w to over 85 % w/w. The versatility of CVD allows for a wide variety of metals to be deposited including copper, titanium, nickel, silver, tungsten, palladium, platinum, ruthenium, rhodium, and iridium. One preferred demonstration involved using platinum depositions to improve the electrical properties of metal-seeded CNTs across all mass loadings studied. Moreover, when the metal-seeded CNT wires were electroplated with copper, densified, and annealed under hydrogen/argon; the result was a Cu-CNT hybrid conductor with the highest conductivities reported to date. The good interconnection of the metal and CNT portions results in a stable conductor. An electrical conductivity of 16-20 MS/m was achieved for multiple Cu-CNT hybrid conductors at 150 °C, which exceeds the program goal for conductivity of greater than 15 MS/m for a 1 m long CNT wire. Overall, the research outcomes from the project showed improvements in CNT wire fabrication from roll-to-roll CVD grown carbon nanotube wires using nanometal interconnects to bridge CNT-to-CNT junctions, thus mitigating network contact resistances. Advanced nanometal interconnected carbon conductors (NICCs) were developed as a means to achieve novel light-weight wiring appropriate for applications that require the conductivity of metallic (i.e., Cu, Al, etc.) wires at elevated temperatures. High conductivity, low TCR electrical conductors such as the nanometal interconnected Cu-CNT hybrids have numerous future applications towards high efficiency motors, generators, and transformers. Specifically, the advanced wires have the ability to operate at lower resistance during conditions for standard electric motors at 150 °C, which would improve the electrical efficiency while lowering energy needs.
Insect sex pheromones are chemical compounds that insects release to attract their partners over distances of hundreds of meters or even kilometers, in complete darkness and without any audible signals. Use of synthetic forms of key compounds have in some cases become an essential component of monitoring and/or managing key pests of agricultural crops, including navel orangeworm (Amyelois transitella) (NOW) in California tree nuts. These pheromone-based strategies can include monitoring, mating disruption, mass trapping, attract-and-kill and push-pull. There are currently multiple commercially available mating disruption products available for NOW and recent studies have demonstrated that they can be effectively used to reduce crop damage by this pest. Just how mating disruption works is not fully established and likely varies across products and target species. For instance, the extent to which synthetic pheromones compete with natural pheromone is not well understood, or in the case of monitoring, how efficaciously the insect follows the diffusing plumes, especially across large blocks and at plot borders. Furthermore, there may be specific conditions under which poor or impeded diffusion of synthetic pheromone diminishes the disruption effect, which could result in some males effectively locating females for reproduction. At the same time, many commercially available synthetic lures are also available, and growers have been effectively using them to track population development – although the attractive range of these lures remains unclear. That is, while pheromone lures can attract many moths, the relationship between trap capture and local populations, much less crop damage, remains unclear. Regardless of the emission source (aerosol puffers, meso emitters, lures etc.), we currently lack the ability to fully understand how these synthetic pheromone compounds diffuse away from their point-source of emission – and subsequently how this might affect the efficacy of mating disruption and/or the accuracy of monitoring efforts. Raman spectroscopy (RS) may offer a means to detect synthetic pheromone because of its analytical properties and recent technological advancements. In combination with nanostructured probes, RS has a demonstrated capability to detect volatiles at extremely low concentrations. While other methods for detecting pheromones have proven to be successful, such analytical methods like mass spectrometry (MS) gas chromatography (GC) require sampling in the fields and are not real-time. Other methods such as EAG/ EAD are also invasive (use antennas) but are of course valuable. Opto-electronic, micro and nano sensors just recently enticed interest. We fall in this latter category, proposing the use of optical spectroscopy (Raman) in a fiber aided by nanostructures to provide a chemical fingerprint without transductions and in real-time (i.e. no sampling). To date, we have been able to generate Raman signatures for the main chemicals in synthetic pheromones (a first for science, to the best of our knowledge), observe their trends at different concentrations, and also provide a clear distinction of such signatures in mixed samples via Principal Component Analysis (PCA) – all of which strengthens our conclusions on the ability of RS to detect emissions in more realistic conditions. We also established processes to enhance the detection sensitivity via preconcentration with Solid Phase Micro Extraction (SPME) fibers and Surface Enhanced Raman Spectroscopy (SERS) to increase the overall optical signals. Furthermore, we explored limitations and requirements for detecting from dispensers, lures, or live moths, including Dept. of Energy and CA State regulatory approvals. Finally, we setup some of our first “orchard in a box” experiments to measure pheromone diffusion along with developing appropriate models.
The National Ignition Facility (NIF) is the world's largest energetic laser whose missions include sresearch in inertial confinement fusion, experiments for the Stockpile Stewardship, and other high fluence laser research. Due to the high fluence of lasers, extreme environmental conditions, and radiation presence, all mechanical parts (metal, plastic, etc.) installed on NIF must be precision cleaned. Precision cleaned parts are also required in supporting facilities like the Optics Processing Facility (OPF). Small debris inside the laser beams or on optics can cause optic damage, interfere with diagnostics, and interfere with experiments.
The objective of this research is to develop a hybrid physics-based/data-driven forecast model to improve direct normal and global horizontal irradiance (DNI and GHI) prediction for horizons ranging from 1 to 72 hours. Project objectives also address key gaps in state-of-the-art solar forecasting: accurate probabilistic solar forecasts and the forecasting of large irradiance ramps (ramp onset and magnitude). The proposed model ensembles Numerical Weather Prediction (NWP) forecasts, determinist physics-based algorithms, and new-generation cloud cover products (high-resolution rapid refresh satellite images and Large Eddy Simulations). The result is the Hybrid Adaptive Input Model Objective Selection (HAIMOS) ensemble model. HAIMOS blends state of the art machine learning methodologies with physics-based models for cloud cover and cloud optical depth forecasts. The technical activities followed a two-pronged strategy. First, the preprocessing of data, the selection of inputs to the nonlinear approximators, the type of approximator and objective functions, and post-processing ensembling techniques included in HAIMOS were all optimized adaptively to find the best model for a specific goal (reduce DNI/GHI forecast error, improve the prediction of ramp onset, etc.). Second, a large effort was put in improving cloud identification and the forecast of cloud cover and cloud optical depth. To this end, new-generation cloud parametrization products were developed in this work. These include improved algorithms to assist in cloud identification, cloud classification and cloud parametrization from satellite images – three key factors in the accuracy of 1 to 6-hours irradiance forecasts and prediction of ramp onset. Furthermore, we also included cloud information extracted from high resolution rapid refresh satellite images (GOES-16) and Large Eddy Simulations (LES). LES was used to model the atmosphere in detail over locations of interest and produce cloud optical depth forecasts. Once these data streams were validated, they were used as input data to the HAIMOS forecast. The model was developed using data from several climatologically distinct locations with potential for high solar penetration. In the last year of the project, we conducted a validation campaign according to the guidelines stipulated by the Topic Area 1 project as described in the FOA. This effort brings, for the first time, proven machine-learning methodologies for generating state-of-the-art solar forecasts interweaved with detailed physics-based models for cloud detection, and cloud optical depth forecasts. HAIMOS will generate accurate irradiance probabilistic forecast to assist in reducing solar generation prediction error. Globally optimized solar forecast models are more likely to impact solar energy stakeholders. The goal of this project was to increase the state-of-the-art forecast skill from their present values of 10 to 35%. At the end of the project, we achieved between 30% and 50% forecast skill across a wide range of horizons for both GHI and DNI.