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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

ASMS 2024 Investigation of Uranyl Perchlorate Anion Complexes in the Gas Phase via Infrared Multiphoton Dissociation and Collision Induced Dissociation

Investigation of Uranyl Perchlorate Anion Complexes in the Gas Phase via Infrared Multiphoton Dissociation and Collision Induced Dissociation Brittany D. M. Hodges, Christopher A. Zarzana, JungSoo Kim, Jonathan Martens, and W. C. M. Berden Introduction (120 words max) Effects of electronic structure on chemical bonding and reactivity play critical roles shaping the chemical bonding and reactivity behaviors of heavy elements. Understanding the role of f electrons in bond formation between the actinide-series elements like uranium with other ligands is critical for solving technical challenges associated with these heavy elements, important to nuclear fuel cycles, efficient separations of rare earth metals, and understanding the chemistry of stored nuclear fuels and related environmental management sites. In this study, we further examine the interactions between uranyl and the perchlorate ion in order to understand the structures of these ions through the use of IRMPD. Here we report the IRMPD spectra of [UO2(ClO4)3]-, [UO3(ClO4)2]-, and a proposed transition state. Methods (120 word max) IRMPD spectra and CID spectra were acquired using a Bruker amaZon QIT/MS instrument at the Free-Electron Lasers for Infrared eXperiments (FELIX) laboratory at Radboud University. The FELIX QIT/MS is modified to allow for the high-intensity tunable IR beam from FELIX to be directed into the ion packet, resulting in multiphoton dissociation that is measured only when the IR frequency is in resonance with an adequately high absorption vibrational mode of the mass-selected complex. DFT geometry optimizations and frequency calculations using the Gaussian suite of programs were performed using B3LYP, TPSSh, and PBE0 level of theory with 6-31+G(d) basis for the O, C, H, and N atoms and the SDD basis set for U. The SDD basis set employs the Stuttgart/Dresden effective core potential. Preliminary Data or Plenary Speakers Abstract (300 words max) Metal ion clusters of uranyl perchlorate were formed via direct electrospray ionization. For each metal ligand complex of interest, the parent ion was isolated and collision induced dissociation fragmentation and Infrared Multiphoton Dissociation (IRMPD) fragmentation spectra were acquired. Results presented here are the first look at the IRMPD spectra of [UO3(ClO4)2]-, [UO2(ClO4)3]-. Structures were examined using Gaussian at different levels of theory B3LYP level of theory, TPPSh and PBE0 levels, to reflect the behaviors of uranium metal ligand complexes most accurately. In these structures, we identified an overlap between each of these uranyl stretches resulting in their largely being obscured by a perchlorate mode. The CID product spectra agree with similar structures reported by Groenewold for uranyl nitrate in 2006 (10.1021/ja058106n).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Fabrication and Scale-Up of Porous Polybenzimidazole (PBI) Supports for Gas Separation Composite Membranes

Industrial gas separation often uses thin film composite (TFC) membranes comprising a porous support overlaid with a single-/multi-layer gas-selective thin film. An optimal porous support should possess high surface porosity to minimize gas transport resistance and nano-sized pores to ease pore penetration occurring during the thin film coating process. Good chemical and thermal stabilities are essential to withstand the aggressive solvents and heat required for the thin film coating and curing. However, few porous membranes satisfy all these requirements. This study presents a scalable membrane formation method of making highly porous polybenzimidazole (PBI) supports via non-solvent induced phase separation. This presentation also details the scale-up fabrication of PBI supports using a custom roll-to-roll membrane casting machine.

gas separation↗
DOE OSTI2026

Precision Kinematic Sunyaev--Zel'dovich Measurements Across Halo Mass and Redshift with DESI DR2 and ACT DR6: Part II. Bright Galaxy Survey and Emission-Line Galaxies

We present the first high-significance spectroscopic stacked kinetic Sunyaev-Zel'dovich (kSZ) measurements of circumgalactic gas profiles for both Bright Galaxy Survey (BGS) and Emission Line Galaxy (ELG) tracers, combining DESI Data Release 2 with ACT Data Release 6. Using reconstructed line-of-sight velocities from the DESI galaxies and high-resolution ACT temperature maps, we detect the kSZ signal at high significance, reaching signal-to-noise ratios of up to $\sim$9 for BGS and $\sim$7.5 for ELGs in optimal stellar-mass selections. Together with the LRG measurements presented in Paper I, these constitute the most significant kSZ detections from any spectroscopic survey to date. We perform the analysis in both real and harmonic space, obtaining consistent results. By splitting both tracers into stellar-mass bins, we study the scaling of the kSZ amplitude with galaxy properties. Combining the kSZ measurements with ACT Data Release 6 (DR6) CMB lensing maps enables a joint calibration of the galaxy-halo connection and the gas fractions of host halos. For the BGS galaxies, we observe low gas fractions around the virial radius relative to standard expectations, likely attributable to active galactic nuclei (AGN) activity. We find some evidence for higher-mass halos retaining a larger fraction of their baryons, consistent with more efficient feedback in lower-mass systems. For the ELG sample, dominated by blue, star-forming galaxies, we provide the first detection of the gas distribution in ELG host halos. The ELGs appear to exhibit relatively high gas fractions, which points to the possibility of weaker feedback (due to e.g. low AGN and supernova feedback activity) at their mass scale. Finally, we present generalized Navarro-Frenk-White (GNFW) fits to the harmonic-space measurements, providing a compact parametrization of gas profiles for forward modeling in large-scale structure analyses.

Hadzhiyska, B. [Cambridge U.; Cambridge U., Inst. ↗
DOE OSTI2024

Nuclear Technology Selection and Preliminary Integration at St. Charles and Carrollton Chemical Plants

Nuclear energy is identified as a valuable candidate for decarbonizing industrial facilities as a non-emitting baseload energy source capable of dispatching energy as needed for an integrated facility. As industrial facilities are unique, analyzing what nuclear technology is the best candidate should be evaluated on a case-by-case basis. For two identified facilities of the Dow Chemical Company, an ideal mix of nuclear energy resources is calculated, and integration strategies are put forward. This research was funded through a Gateway for Accelerated Innovation in Nuclear (GAIN) Nuclear Energy Voucher, specifically seeking near-term engineering solutions for the technical issue of introducing nuclear generated energy into the Dow chemical facilities. The research is split into four main phases: background on reactors and chemical facilities, preliminary engineering analysis determining how reactor technologies may be integrated with specific facilities, site characterization and discrete hazards analysis, and finally an optimization of reactor-facility selection through the use of Framework for Optimization of ResourCes and Economics (FORCE) tools.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Molecular layer deposition (MLD) modified SSZ-13 membrane for greatly enhanced H 2 separation

Zeolite membranes with high thermal stability and well-fined pores are attractive for gas separation. However, the defects are almost inevitable in zeolite membranes. Moreover, fine-tuning zeolitic pores for precise molecular separation is challenging. In this study, molecular layer deposition (MLD) was employed to deposit ultrathin microporous coating on SSZ-13 zeolite membrane. The MLD modified SSZ-13 composite membrane was highly selective for H 2 separation. With optimized MLD cycles, H 2 /N 2 and H 2 /CH 4 ideal selectivities as high as 35.6 and 427, respectively, were obtained, in strong contrast with approximately 5 for base SSZ-13 membrane. Our results suggest that MLD is a very promising technology to precisely modify the pore size of zeolite membranes, while minimizing flow through non-selective defects, for gas separation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Smart Microbial Cell Technology: A high-throughput platform to optimize biocatalysts

Los Alamos National Laboratory developed an ultra-high-throughput screening platform to engineer custom biocatalysts that enhance the rate of chemical reactions critical in pharmaceuticals, renewable energy, and environmental cleanup. Current methods to find biocatalysts are slow. This platform scans genetic variations to optimize a single enzyme or microbial cell to generate a product efficiently. It selects rare mutations needed for biocatalyst optimization orders of magnitude faster than current screening methods. A custom sensor reporter gene circuit causes cells to fluoresce when they are making the target product. When coupled to flow cytometry, a million biocatalyst variants can be screened in hours.

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

Unraveling 1-Hexene Hydrogenation over Dilute Pd-in-Au Alloys

Dilute Pd-in-Au alloys are valuable materials for selectively hydrogenating alkynes and isomerizing alkenes. By diluting Pd in a Au host, the selectivity toward semihydrogenated alkene isomers can be significantly enhanced and the unfavorable overhydrogenation to alkanes is suppressed. However, a detailed mechanistic study on the origin of the enhanced alkene selectivity over dilute alloy catalysts is still missing. Here, we combine experiment and theory to unravel the reaction mechanism, identifying rate-limiting and selectivity-controlling steps in 1-hexene hydrogenation over dilute Pd-in-Au catalysts. Using isotope-exchange hydrogenation experiments, we show that 1-hexene and hydrogen over a bimetallic Pd4Au96 in silica catalyst preferentially form 1-hexene isomers, (trans and cis) 2- and 3-hexene and only a small amounts of hexane. The reaction is consistent with a Horiuti–Polanyi mechanism, similar to a monometallic Pd nanoparticle catalyst. Computation of the free-energy profiles for 1-hexene hydrogenation and isomerization over a single Pd atom in a Au surface using first principles calculations indicated that the isomerization of 1-hexene to 2-hexene is energetically favorable due to the relatively large barrier for H2 dissociation preventing hydrogenation to n-hexane. Microkinetic modeling established that H2 dissociation on the single-atom Pd sites and H spillover from these sites onto the Au host are rate-limiting and key in steering the selectivity of dilute Pd-in-Au alloys toward the hexene isomers. Furthermore, the mechanistic insights from this study contribute to the rational design of optimized dilute alloy catalysts for selective alkene isomerization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties

Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery.

3D molecular generation↗
DOE OSTI2017

Intermetallic Pd3Pb Nanowire Networks Boost Ethanol Oxidation and Oxygen Reduction Reaction with Significantly Improved Methanol Tolerance

Intermetallic nanocrystals are currently receiving extensive attentions due to their well- defined crystal structures, highly ordered atomic distribution and superior structural stability that endow them with optimized catalytic activities, stabilities and high selectivity as electrocatalysts for fuel cells. Here, for the first time, we reported the facile synthesis of intermetallic Pd3Pb nanowire networks (IM-Pd3Pb NNs) with a one-step wet-chemical strategy at a relatively low temperature (i.e. 170 °C) in 1 h. The as-prepared IM-Pd3Pb NNs exhibited superior bifunctional catalytic performances toward oxygen reduction reaction (ORR) and ethanol oxidation reaction (EtOR) compared to commercial Pt/C and Pd black, respectively. Significantly, IM-Pd3Pb NNs also showed excellent methanol- and CO-tolerant ability as ORR cathode and EtOR anode electrocatalysts, respectively. The electrochemical active surface area and mass activity of IM-Pd3Pb NNs are about 3.4 times and 2 times higher than Pd black toward EtOR, respectively. As the Pt-free bifunctional electrocatalysts, 3D IM-Pd3Pb architectures with exceptional catalytic performances hold great promise in various applica-tions of energy conversion and storage devices, sensors, electronics, optics and so on.

Shi, Qiurong↗
DOE OSTI2019

Entropy-based closure for probabilistic learning on manifolds

In a recent paper, the authors proposed a general methodology for probabilistic learning on manifolds. The method was used to generate numerical samples that are statistically consistent with an existing dataset construed as a realization from a non-Gaussian random vector. The manifold structure is learned using diffusion manifolds and the statistical sample generation is accomplished using a projected Itˆo stochastic differential equation. This probabilistic learning approach has been extended to polynomial chaos representation of databases on manifolds and to probabilistic nonconvex constrained optimization with a fixed budget of function evaluations. The methodology introduces an isotropic-diffusion kernel with hyperparameter ". Currently, " is more or less arbitrarily chosen. In this paper, we propose a selection criterion for identifying an optimal value of ", based on a maximum entropy argument. The result is a comprehensive, closed, probabilistic model for characterizing data sets with hidden constraints. This entropy argument ensures that out of all possible models, this is the one that is the most uncertain beyond any specified constraints, which is selected. Applications are presented for several databases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Comparing Sensor Fusion and Multimodal Chemometric Models for Monitoring U(VI) in Complex Environments Representative of Irradiated Nuclear Fuel

Optical sensors and chemometric models were leveraged for the quantification of uranium(VI) (0–100 μg mL –1 ), europium (0–150 μg mL –1 ), samarium (0–250 μg mL –1 ), praseodymium (0–350 μg mL –1 ), neodymium (0–1000 μg mL –1 ), and HNO 3 (2–4 M) with varying corrosion product (iron, nickel, and chromium) levels using laser fluorescence, Raman scattering, and ultraviolet–visible–near-infrared absorption spectra. In this paper, an efficient approach to developing and evaluating tens of thousands of partial least-squares regression (PLSR) models, built from fused optical spectra or multimodal acquisitions, is discussed. Each PLSR model was optimized with unique preprocessing combinations, and features were selected using genetic algorithm filters. The 7-factor D-optimal design training set contained just 55 samples to minimize the number of samples. The performance of PLSR models was evaluated by using an automated latent variable selection script. PLS1 regression models tailored to each species outperformed a global PLS2 model. PLS1 models built using fused spectra data and a multimodal (i.e., analyzed separately) approach yielded similar information, resulting in percent root-mean-square error of prediction values of 0.9–5.7% for the seven factors. Further, the optical techniques and data processing strategies established in this study allow for the direct analysis of numerous species without measuring luminescence lifetimes or relying on a standard addition approach, making it optimal for near-real-time, in situ measurements. Nuclear reactor modeling helped bound training set conditions and identified elemental ratios of lanthanide fission products to characterize the burnup of irradiated nuclear fuel. Leveraging fluorescence, spectrophotometry, experimental design, and chemometrics can enable the remote quantification and characterization of complex systems with numerous species, monitor system performance, help identify the source of materials, and enable rapid high-throughput experiments in a variety of industrial processes and fundamental studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Optimization of a High Pressure Turbine Blade and Sector-Based Annular Rig Design for Supercritical CO2 Power Cycle Representative Testing

Abstract As part of the ongoing research into the design of hardware for zero emission cycles, a first-stage high-pressure turbine (HPT) blade is optimized for a 300 MWe supercritical CO2 (sCO2) power cycle using the surrogate-assisted genetic algorithm optimizer in Numeca FINE/Design three-dimensional with objectives of increasing efficiency and decreasing heat load to the blade. Supercritical CO2 property tables are constructed from NIST REFPROP data for the condensable gas simulation in FINE/Turbo. A detailed mesh sensitivity study is performed for a baseline design to identify the proper-grid refinement and efficiently allocate resources for the optimization. Seventy design variables are selected for the initial population generation. Self-organizing maps are then used to focus the design variables on the most important ones affecting the objective functions. The optimization results in approximately 3000 three-dimensional Reynolds Averaged Navier Stokes simulations of different blade shapes with increases in efficiency of up to 0.85% and decreases in heat load of 14%. Families of blade shapes are identified for experimental testing in an annular rig at the Purdue Experimental Turbine Aerothermal Laboratory. A design to adapt the annular cascade for testing optimized geometries is introduced, which features eccentric radius sectors allowing for scaled-up geometries of sCO2 optimized blade profiles to be tested at design cycle representative conditions at high Reynolds numbers in dry air. Analysis into the effects of Reynolds number, working fluid, and geometric relations are presented to prove the efficacy of the test method.

Engineering↗
DOE OSTI2020

Optimal Operation of a Hybrid Hydraulic Electric Architecture (HHEA) for Off-Road Vehicles Over Discrete Operating Decisions

Many off-highway machines including construction and agriculture equipment use hydraulics for power transmission and throttling as a means for control. Trends towards better efficiency and electrification have led to the creation of a novel Hybrid Hydraulic-Electric Architecture (HHEA) which could significantly reduce energy consumption and maintain control performance, even in machines that are too large to be directly electrified. This is achieved by using a set of common pressure rails to transmit the majority of power via power dense hydraulics and modulating the power with small electric motor-drives to achieve precise control. This paper proposes a computationally efficient method for computing the optimal sequence of pressure rail selections for the HHEA over finite drive cycles. This is useful for fairly comparing the novel architecture’s energy performance to existing architectures and for use in iterative optimal design of the architecture. The optimal control technique makes use of a static model of the architecture and losses. Constraints are enforced such that the drive cycle is repeatable. The constrained optimal operation is solved using a Lagrange multiplier technique that transforms the optimization into a small set of sub-problems by considering combinations of active constraints. Each of these sub-problems can be solved efficiently because loss calculations for all time steps can be computed in parallel. A case study of an off-road construction machine demon-strates that the HHEA reduces energy consumption by 2/3 compared to the baseline load sensing architecture.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Leveraging Multiple Connected Traffic Light Signals in an Energy-Efficient Speed Planner

Connecting automated vehicles to traffic lights can lead to significant energy savings by enabling them to pass through intersections in an energy-efficient way without unnecessary stops. A cellular-based communication system connecting multiple traffic lights can help realize the full potential of energy-efficient driving at intersections. Thus, we propose a hierarchical speed planner that can leverage information from multiple connected traffic lights. The proposed speed planner consists of two modules: a green window selector and a reference trajectory generator. The green window selector, based on Dijkstra’s algorithm, finds a series of "green windows" for connected traffic lights that builds an energy-optimal path for vehicles to follow. The reference trajectory generator finds optimal entering times, based on the selected green window at each intersection, and then computes reference trajectories. Deriving and using analytical optimal entering speeds as a function of entering times allows us to guarantee the computational simplicity suitable for real-time implementation. We also demonstrate how to balance energy and traffic flow perspectives in the reference trajectory generator. Lastly, a high-fidelity simulation framework is used to evaluate the proposed speed planner and quantify the extent to which it can save energy in various real-world urban route scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Dispersion-enhanced sequential batch sampling for adaptive contour estimation

In computer simulation and optimal design, sequential batch sampling offers an appealing way to iteratively stipulate optimal sampling points based upon existing selections and efficiently construct surrogate modeling. Nonetheless, the issue of near duplicates poses tremendous quandary for sequential learning. It refers to the situation that selected critical points cluster together in each sampling batch, which are individually but not collectively informative towards the optimal design. Near duplicates severely diminish the computational efficiency as they barely contribute extra information towards update of the surrogate. To address this issue, we impose a dispersion criterion on concurrent selection of sampling points, which essentially forces a sparse distribution of critical points in each batch, and demonstrate the effectiveness of this approach in adaptive contour estimation. Specifically, we adopt Gaussian process surrogate to emulate the simulator, acquire variance reduction of the critical region from new sampling points as a dispersion criterion, and combine it with the modified expected improvement (EI) function for critical batch selection. The critical region here is the proximity of the contour of interest. This proposed approach is vindicated in numerical examples of a two-dimensional four-branch function, a four-dimensional function with a disjoint contour of interest and a time-delay dynamic system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Analytical method for nitroaromatic explosives in radiologically contaminated soil for ISO/IEC 17025 accreditation

Here, unique hazards are presented in the analysis of radiologically contaminated samples. Strenuous safety and security precautions must be in place to protect the analyst, laboratory, and instrumentation used to perform analyses. A validated method has been optimized for the analysis of select nitroaromatic explosives and degradative products using gas chromatography/mass spectrometry via sonication extraction of radiologically contaminated soils, for samples requiring ISO/IEC 17025 laboratory conformance. Target analytes included 2-nitrotoluene, 4-nitrotoluene, 2,6-dinitrotoluene, and 2,4,6-trinitrotoluene, as well as the degradative product 4-amino-2,6-dinitrotoluene. Analytes were extracted from soil in methylene chloride by sonication. Administrative and engineering controls, as well as instrument automation and quality control measures, were utilized to minimize potential human exposure to radiation at all times and at all stages of analysis, from receiving through disposition. Though thermal instability increased uncertainties of these selected compounds, a mean lower quantitative limit of 2.37 µg/mL and mean accuracy of 2.3% relative error and 3.1% relative standard deviation were achieved. Quadratic regression was found to be optimal for calibration of all analytes, with compounds of lower hydrophobicity displaying greater parabolic curve. Blind proficiency testing (PT) of spiked soil samples demonstrated a mean relative error of 9.8%. Matrix spiked analyses of PT samples demonstrated that 99% recovery of target analytes was achieved. To the knowledge of the authors, this represents the first safe, accurate, and reproducible quantitative method for nitroaromatic explosives in soil for specific use on radiologically contaminated samples within the constraints of a nuclear analytical lab.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Uniting Surface Properties With Hydrodynamic Roughness in Shallow Overland Flow Models

Describing flow resistance from the properties of an underlying surface is a challenge in surface hydrology. Runoff models must specify a resistance formulation or “roughness scheme”—describing the functional relationship between flow resistance and flow depth/velocity—and its parameters. Uncertainty in runoff predictions derives from both the selected roughness scheme (e.g., Darcy Weisbach, Manning's, or laminar flow equations), and its parameterization with a roughness coefficient (e.g., Manning's n). Both choices are informed by model calibration to data, usually discharge, and, if available, velocity. In this study, a Saint Venant Equation-based runoff model is calibrated to discharge and velocity data from 112 rainfall simulator experiments. The results are used to identify the optimal roughness scheme among four widely-used options for each experiment, and to explore whether surface properties can be used to select the optimal roughness scheme and its coefficient. Among the tested roughness schemes, a transitional flow equation provided the best fit to the plurality of experiments. The most suitable roughness scheme for a given experiment was not related to measured surface properties. Regression models predicted the calibrated roughness coefficients with adjusted values between 0.48 and 0.54, depending on the roughness scheme used. Litter cover was the best predictor of the roughness coefficient, followed by soil cover and average canopy gap size. The results suggest that selection of an optimal roughness scheme based on surface properties alone remains difficult, but that once a scheme is selected, roughness coefficients can be estimated from surface properties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Big Adaptive Rotor Phase I (Final Report)

The Big Adaptive Rotor (BAR) project was initiate by DOE in 2018 with the goal of identifying novel technologies that can enable large (>100m) blades for low specific power (SP) turbines. Five distinct tasks were completed to achieve this goal: 1. Assess trends, impacts, and value of low-SP turbines, 2. Wind turbine blade cost reduction roadmap study, 3. Research and Development (R&D) opportunity screening, 4. Detailed design and analysis, and 5. Low-cost carbon fiber. These tasks were completed by the national lab team consisting of Sandia National Laboratories (SNL), the National Renewable Energy Laboratory (NREL), and Lawrence Berkeley National Laboratory (LBNL). The objective of Task 1 was to assess the historical trends of low-SP of onshore deployments, and to assess the impact and value of low-SP turbines. Analysis under this task showed that there is significant value for low-SP turbines, especially in markets where there is a higher saturation of wind energy on the grid. This has to do with the fact that low-SP turbines have increased capacity factors, and can contribute more reliable energy to the grid, even in lower wind conditions. This bodes well as higher renewable scenarios are likely in the coming years. Task 2 was a detailed analysis on the logistical challenges of deploying very large (>100m) onshore blade. This work was completed by experts at DNV-GL. The report looked at various ways to get around the current transportation constraints which are estimated to be around 75m in length. The report concluded that there are viable solutions to this issue including: segmented blades (which the industry is already pursuing), lighter than air (LTA) transportation, on-site manufacturing, and controlled bending of blades on rail. The final option was recommended for further analysis and study by DOE and the national labs because keeping a single piece blade reduces manufacturing and operations and maintenance (O&M) costs. Task 3 focused on identifying novel concepts that are capable of enabling a cost effective 5MW 206m rotor for onshore deployment. The findings from the first two tasks were considered in the analysis, in that the concepts identified must be able to overcome the transportation logistics challenges. Around 20 concepts were identified and evaluated by experts within the industry. Additionally, science and engineering challenges were identified for each concept. Based on these evaluations six concepts were deemed the most impactful and were selected for further analysis in Task 4. The concepts were: highly flexible rail transportable blades, downwind rotors, distributed aerodynamic control (DAC) devices, inflatable blade, bi-wing blade, and 4/5 bladed rotors. Task 4 conducted detailed design, optimization, and analysis on the selected BAR concepts. A modeling gaps analysis was conducted, and modeling improvements were implemented that allowed for the study and design of the novel concepts. The BAR team established a set of baseline designs by which to compare the selected designs through a technoeconomic analysis. It was found that the highly flexible rail transportable blade, the downwind rotor, and the DAC devices have the most promise to deliver the BAR targets. A Phase II for BAR has been proposed to further mature these concepts and address the open science and engineering challenges identified in Task 3. Task 5 conducted research on optimized carbon fiber materials that were used throughout the BAR Phase I project. Overall, the BAR project has identified low-SP turbines as important to continued LCOE reductions for onshore turbines. Furthermore, the project has identified viable solutions to the technical and logistical challenges to realizing these goals. The most promising technologies that were identified in Phase I of the project will be further matured and de-risked in Phase II of the BAR project.

17 WIND ENERGY↗