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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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Network-Cognizant Time-Coupled Aggregate Flexibility of Distribution Systems Under Uncertainties
Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. To exploit this flexibility and to use the capacity potential of aggregate DERs, feasible substation power injection trajectories need to be efficiently characterized. This paper provides an ellipsoidal inner approximation of the set of feasible power injection trajectories at the substation such that for any point in the set, there exists a feasible disaggregation strategy of DERs for any load uncertainty realization. The problem is formulated as one of finding the robust maximum volume ellipsoid inside the flexibility region under uncertainty. Though the problem is NP-hard even in the deterministic case, this paper derives novel approximations of the resulting adaptive robust optimization problem based on optimal second-stage policies. The proposed approach yields less conservative flexibility characterization than existing flexibility region approximation formulations. The efficacy of the proposed method is demonstrated on a realistic distribution feeder.
Development of vegetative oil sorghum: From lab‐to‐field
Summary Biomass crops engineered to accumulate energy‐dense triacylglycerols (TAG or ‘vegetable oils’) in their vegetative tissues have emerged as potential feedstocks to meet the growing demand for renewable diesel and sustainable aviation fuel (SAF). Unlike oil palm and oilseed crops, the current commercial sources of TAG, vegetative tissues, such as leaves and stems, only transiently accumulate TAG. In this report, we used grain (Texas430 or TX430) and sugar‐accumulating ‘sweet’ (Ramada) genotypes of sorghum, a high‐yielding, environmentally resilient biomass crop, to accumulate TAG in leaves and stems. We initially tested several gene combinations for a ‘push‐pull‐protect’ strategy. The top TAG‐yielding constructs contained five oil transgenes for a sorghum WRINKLED1 transcription factor (‘push’), a Cuphea viscosissima diacylglycerol acyltransferase (DGAT; ‘pull’), a modified sesame oleosin (‘protect’) and two combinations of specialized Cuphea lysophosphatidic acid acyltransferases and medium‐chain acyl‐acyl carrier protein thioesterases. Though intended to generate oils with medium‐chain fatty acids, engineered lines accumulated oleic acid‐rich oil to amounts of up to 2.5% DW in leaves and 2.0% DW in stems in the greenhouse, 36‐fold and 49‐fold increases relative to wild‐type (WT) plants, respectively. Under field conditions, the top‐performing event accumulated TAG to amount to 5.5% DW in leaves and 3.5% DW in stems, 78‐fold and 58‐fold increases, respectively, relative to WT TX430. Transcriptomic and fluxomic analyses revealed potential bottlenecks for increased TAG accumulation. Overall, our studies highlight the utility of a lab‐to‐field pipeline coupled with systems biology studies to deliver high vegetative oil sorghum for SAF and renewable diesel production.
Neural Networks for Supercapacitor Modeling
This software implements a one-dimensional model of a supercapacitor and calibrates it to experimental data. A system of coupled partial differential equation models the salt concentration and the ionic and electronic potentials. The term in the equation modeling the capacitance contains a neural network, whose parameters are optimized to fit the provided experimental data. The software is written in python using the open-source library JAX to calculate the gradients needed for the optimization.
Optimization of thermal barrier coating performance and durability over a drive cycle
A methodology to thermo-mechanically optimize a piston thermal barrier coating over a full drive cycle was established. The optimization objective was to minimize the heat transfer to the engine wall while maintaining structural integrity of the coating. Over 800 candidate materials were investigated and the optimization required more than one million non-road transient drive cycle calculations; real materials were investigated to ensure a realizable result and the existence of thermal and mechanical properties. High computational efficiency was achieved using a recently developed analytical heat transfer technique for multilayer engine walls. An uncoupled approach was utilized for the optimization, wherein the gas temperature and heat transfer coefficient profiles from a fully coupled and calibrated baseline model over the 20-min drive cycle were employed. The coating/piston interface temperature was constrained to be below the maximum piston service temperature limit. The durability was assessed using a recently developed analytical coating delamination framework for engine in-cylinder coatings based on the energy release rate when a crack forms. Results are presented for a mechanically unconstrained optimization and for cases constrained to three fixed levels of drive-cycle maximum energy release rate, and also constrained by the individual material’s toughness. The best-performing coating materials identified were verified using the fully coupled system-level model, which compared well to the uncoupled predictions. A study on the effect of adding a sealing layer to some high-performing, but porous, coatings showed a reduction in fuel consumption benefit and an increased exhaust temperature over the cycle, but the system still outperformed the uncoated case. The results of the study elucidate the importance of including engine performance and mechanical failure considerations in thermal barrier coating design.
Investigation of modified uni-traveling carrier photodiode for cryogenic microwave photonic links
Quantum devices present the potential for unparalleled computing and communications capabilities; however, the cryogenic temperatures required to successfully control and read out many qubit platforms can prove to be very challenging to scale. Recently, there has emerged an interest in using microwave photonics to deliver control signals down to ultracold stages via optical fiber, thereby reducing thermal load and facilitating dense wavelength multiplexing. Photodetectors can then convert this optical energy to electrical signals for qubit control. The fidelity of the quantum operations of interest therefore depend heavily upon the characteristics of the photodiode, yet experimental demonstrations of fiber-coupled photodetection systems at low temperatures are relatively few in number, leaving important open questions regarding how specific detectors may perform in real-world cryogenic settings. In this work, we examine a highly linear modified uni-traveling carrier photodiode (MUTC-PD) under C-band illumination (1530–1565 nm) at three temperature regimes (300 K, 80 K, and ∼4 K) and multiple bias conditions. Our findings of reduced responsivity but preserved bandwidth are consistent with previous studies, while our saturation tests suggest a variety of potential applications for MUTC-PDs in cryogenic microwave photonics with and without electrical bias. Overall, our results should provide a valuable foundation for the continued and expanding use of this detector technology in quantum information processing.
Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases
A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.
Performance of PIP-II high-beta 650 cryomodule after transatlantic shipping
After shipment to the Daresbury Lab and return to Fermilab, the prototype HB650 cryomodule underwent another phase of 2K RF testing to ascertain any performance issues that may have arisen from the transport of the cryomodule. While measurements taken at room temperature after the conclusion of shipment indicated that there were no negative impacts on cavity alignment, beamline vacuum, or cavity frequency, testing at 2K was required to validate other aspects such as tuner operation, cavity coupling, cryogenic system integrity, and cavity performance. Results of this latest round of limited 2K testing will be presented.
Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) (Field Campaign Report)
The Arctic is rapidly changing, and these changes have substantial societal relevance. First, arctic change is a leading sign of global change, as the warming observed in the Arctic is 2-3 times faster than observed across the rest of the globe. This so-called arctic amplification is due to numerous feedbacks, including those linked to the declining sea ice. Broad arctic changes are also hypothesized to be related to shifts in large-scale circulation patterns that may have implications for mid-latitude weather and ocean circulation. Arctic change is further affecting the oceans through acidification that threatens food supplies. Declining sea ice also opens the Arctic for new resource development, shipping routes, tourism, and other commercial activities. Lastly, the changing Arctic is a hotbed for geopolitical challenges as nations vie for influence and control of this resource-rich and newly accessible region. Clearly the Arctic and its changes are playing a huge role in our Earth both from a geophysical and socio-political standpoint. To navigate, manage, and respond to the challenges associated with arctic change requires a vastly improved understanding of the coupled arctic system, and the drivers and implications of these changes. It is essential to better understand the physical basis for why the arctic sea ice is declining, the associated feedbacks that work to amplify or modulate this decline, and the myriad ways that the Earth system is responding. As a result of these changes and uncertainties, there are increasing societal needs for improved model predictive skill in the Arctic, to address pressing gaps in global climate prediction, to advance forecast skills for regional and hemispheric weather and sea ice, and to ensure robust ecosystem models that realistically link physical and biological systems. To fulfill these core needs requires new, sophisticated, and cross-cutting observations within the rapidly changing arctic ice pack.
Seamless Hybrid-integrated Interconnect NEtwork (SHINE)
Large-scale date centers are becoming increasingly limited by the capacity of interconnects due to the rapid increasing power consumption and bandwidth demands driven by the rigorous down scaling of CMOS critical dimensions in according with the Moore’s Law. As a result of the continuing bandwidth scaling, optical interconnects based on parallel optics, where data are simultaneously transmitted along more than one lane, have becoming a preferred interconnected solution over copper cables. To building an efficient optical interconnect system, optical coupling between different elements, such as optical fibers, photonics chips, is a critical step, which is challenging due to the mode mismatch of varies optical ports. In this project, we proposed and demonstrated an optical interfacing scheme, which provided a light coupling approach with low energy loss, broadband operation, high alignment tolerance and compatible with scalable fabrication and testing. Here, the mode transformation is achieved by a total internal reflection from a chip integrated microscale freefrom surface. Due to the enormous light wavefront manipulating ability associated with the gigantic design space, the freeform couplers platform is universally applicable to a wide range of optical coupling scenarios, such as the interfacing between a waveguide to a fiber, another waveguide, surface normal device, or free space.
Visualization Tool for Comparing Low-Carbon Energy Options
This project developed a web based visualization tool for comparing current and future nuclear fuel cycle options to low-carbon and conventional energy technologies in the United States. While nuclear power is well established as the gold-standard for baseload power production and also as the technology with the lowest overall carbon intensity of any commercial form of electricity generation. This project develop a web-based tool for electricity producers and consumers to compare renewable and conventional energy technologies to the conventional and advanced nuclear fuel cycle options. The work leveraged data on land use, carbon intensity, unit cost and pricing data for renewables from the open literature. Input data from the Advanced Fuel Cycle Cost Basis report will be used for current and advanced nuclear power systems. We coupled these data to energy flow and lifecycle models for user-selected energy generation and storage types. We will display economic comparisons from the perspectives of generators and consumers, as well as carbon production, land use, and reliability comparisons.
Continuous Fiber Bath Treatments at Pilot Scale: A Novel Testbed System
The technical demands on high performance textiles continues to expand, and the research community has answered the call with innovative fiber bath treatments for enhanced properties and sizings for improved composites. However, little translation to greater scale has been demonstrated. To fill this gap, a novel, multifunctional system has been developed to explore polymer fiber treatment methods, the deployment of nanoadditive coatings and other sizings, and smart processing methods at the pilot scale. Continuous tows of fibers can be passed through a temperature-controlled, crossflow bath with an immersion residence time exceeding 2 hours. The modular system is resistant to corrosive solutions, includes additional inline stations for washing and drying after processing, is equipped with a bevy of sensors, pumps, and valves monitored and controlled by a LabView program, and can be fully contained in a standard lab fume hood. This new testbed system, when coupled with high fidelity materials characterization methods, will enable rapid evaluation of new fiber treatment methods at pilot-scale.
Final Report: Next Generation Diamond Drumhead X-Ray Optics
Problem / Opportunity Statement: Future high-impact scientific applications at synchrotron and Free-Electron Laser (FEL) X-ray sources require the development of processing and mounting methods for ultra-thin diamond crystals to meet the most challenging needs of pioneering research at next generation light source facilities. These methods should be capable of manufacturing crystal plates as thin as 10 micrometers over an area of a few mm2, and with a surface cut in different crystal orientations, such as the diamond {100}, {111}, {110} and {113} planes. Project Purpose and Approach: In Phase I, Great Lakes Crystal Technologies (GLCT) partnered with Stonybrook University (SUNY) to created thin diamond drumhead crystals. The purpose of the Phase I project is to transfer the drumhead membrane window etching technology from SUNY to GLCT. Testing of a device using the beamline at Stanford Linear Accelerator Laboratory was initially planned, however, a delay on the part of a third party service provided required us to get beamline testing done at Argonne National Laboratory. Phase I Taskwork: Testing of a high purity diamond plate thinning and etching process was performed. By SUNY and GLCT. Plates were sent to a third party service provider for thinning to ~100 microns, however, delays required that GLCT develop an in-house thinning process to complete the taskwork. GLCT successfully developed this in-house capability even though it was initially planned to be part of the Phase II Taskwork. Multiple practice and deliverable plates were provided to SUNY to be etched into the target drumhead devices. GLCT practiced and refined an in-house plate thinning process and produced five plates with thicknesses in the 60 – 150 micron range. Processing of the plates has begun and will continue in a no-cost extension period necessitated by the third party service provider delay. GLCT also procured and installed a reactive ion etching/inductively coupled plasma system. In addition, one of the thinned plates was measured at the Applied Photon Source at APL for a pre-processing assessment of the crystalline strain. After processing the plate into a drumhead and annealing, a post-processing assessment of the strain will be performed. Phase I Results: The initial data from GLCT’s plate thinning process development, SUNY’s drumhead etching processing, and characterization at APS/ANL demonstrate our teams capabilities to achieve the project goals. Commercial Applications & Benefits: The proposed technology will benefit advanced x-ray science performed at BNL and the 70+ additional advanced beamlines around the world. There are also a number of medical and homeland security applications which could benefit from the technology.
Generalized kinetic model for defect evolution in irradiated materials
A fundamental and open problem in materials science is to determine how the structural properties of irradiated and self-irradiated materials evolve in time. Structural defects generated by irradiation lead to changes in a material’s macroscopic properties, and these macroscopic changes, such as volumetric swelling, can in turn give rise significant alterations of the functionality of a material. Here, we develop a unified analytical model to understand the complex interplay between aggregation, fragmentation, and recombination processes involving mobile clusters of defects such as bubbles, voids, and interstitial atoms in materials subject to radiative sources. Specifically, we employ a mean field approach to derive a system of coupled kinetic equations that describes both the time evolution of the density of each type of defect and how clusters of each defect form and grow as the material ages.
PIP-II High Beta 650 Prototype Cryomodule Assembly Experience and Related Design Optimization
After shipment to the Daresbury Lab and return to Fermilab, the prototype HB650 cryomodule underwent another phase of 2K RF testing to ascertain any performance issues that may have arisen from the transport of the cryomodule. While measurements taken at room temperature after the conclusion of shipment indicated that there were no negative impacts on cavity alignment, beamline vacuum, or cavity frequency, testing at 2K was required to validate other aspects such as tuner operation, cavity coupling, cryogenic system integrity, and cavity performance. Results of this latest round of limited 2K testing will be presented.
CONCENTRATED SOLAR POWER COUPLED WITH WASTE HEAT RECOVERY SYSTEM FOR INNOVATIVE CLINKER CALCINATION PROCESS
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Low Cost High-Performance HRE-Free 3-in-1 Electric Drive Unit
The project team completed work to expand upon American Axle & Manufacturing’s (AAM) baseline technology and develop a high-speed, AC induction electric drive unit (EDU) with total direct-oil-cooling and integrated power electronics. The project accelerated advancements in EDU technology by allowing AAM to build upon the progress made in developing the underlying baseline technologies. The following seven improvements to AAM’s baseline technology were implemented in this research and development program: 1) Increased speed (30k RPM) AC induction motor, 2) Silver-sintering of discrete silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFET) to heat sinks, 3) Electrically insulated rotor bars, 4) Optimized lamination steel, 5) EDU-integrated 650VDC inverter package, 6) Over-molded stator with molded liners, and 7) 650VDC power-dense stator design. The successful development of the technology resulted in a meaningful cost reduction for EDU systems in the market. Coupled with falling battery system costs, lower EDU costs will likely accelerate the market acceptance and related production scaling of battery electric vehicles. The objective of this project was to research, develop, and test a Heavy Rare Earth (HRE)-free 3-In-1 electric drive unit (EDU) that has class leading power density and cost. The key technologies to be developed were anticipated to meet or exceed the Department of Energy (DOE) targets of a cost ≤ $\$$7/kilowatt (kW), power density ≥ 12 kW/liter, and operating voltage ≥ 600 VDC.
Joint modeling of water and energy for resilience and flexibility
This white paper addresses the intersection of energy and water resources for potable water, considering energy-for-water through joint systems modeling. Specifically, we focus on the opportunities to promote joint resilience in water and power systems through coupled modeling to inform decision-makers.