Solar Photovoltaic Awning as Installation Method for Manufactured Homes
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14 SOLAR ENERGY↗
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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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Benefits of the SolarAPP+ Online Permitting Software
The Solar Automated Permit Processing Plus (SolarAPP+) software is an online platform that provides plan review and instantly issues permits for code-compliant residential photovoltaic (PV) systems.
automated permitting↗
5 Easy Steps to SolarAPP+ Adoption
The Solar Automated Permit Processing Plus (SolarAPP+) software is an online platform that provides plan review and instantly issues permits for code-compliant residential photovoltaic (PV) systems. Here's how your jurisdiction can get started.
automated permitting↗
Dual Phase Soft Magnetic Laminates for Low-cost, Non/Reduced-Rare-Earth Containing Electrical Machines
To accelerate the mass market adoption of electric drive vehicles, the key technology barriers in electric motors are (1) magnet cost and rare-earth element price volatility; (2) non-rare-earth electric motor performance; and (3) materials property optimization. The goal of this project was to address these barriers by advancing a unique and innovative dual phase soft magnetic material technology and demonstrating the material in a 30-kW synchronous reluctance motor without using any permanent magnet for electric vehicles. Dual phase magnetic materials offer the electric motor designer the ability to locally control the magnetic saturation level in a motor laminate, while at the same time enhancing the mechanical strength of the laminate material, resulting in an enhancement in motor performance and efficiency. Scalable dual phase soft magnetic laminates manufacturing technologies were developed in collaboration with multiple US manufacturers. 1000 lbs of alloy sheet with a thickness of 0.25mm and width of 280 mm was manufactured within the specifications. Batch sizes of up to 240 laminates per run were produced from the alloy sheet. Two prototype motors with dual phase soft magnetic laminates were designed, built, and tested. The major goal of building the subscale prototype as a pathway to develop scalable manufacturing technologies for the dual phase soft magnetic laminates was met. The additional goal of building and testing the subscale prototype in order to validate the calculated performance with the tested motor performance was also met. For the full-scale 30kW continuous power synchronous reluctance motor prototype, the tested performance met the targets in terms of continuous power at the operating speeds up to 8000 rpm. Post-test studies were conducted and the root causes for the discrepancy between the predicted and tested peak power, continuous power at high speed range, and efficiency were identified. Further modeling study showed that the dual phase rotor machine has a 27% higher torque to active weight ratio than an equivalent performance silicon steel rotor machine. Application space and multiple discussions with traction motor and electric vehicle manufacturers for commercialization of the dual phase soft magnetic material technology were identified and conducted. An initial cost model was established based on the developed manufacturing technologies with the US manufacturers. Future paths for further cost reduction were identified, including increasing the market volume by broadening the applications of the dual phase soft magnetic laminate technology for electric machines in other energy sections such as oil & gas, heating, ventilation, and air conditioning (HVAC), and power generation.
33 ADVANCED PROPULSION SYSTEMS↗
Mechanisms of hardware and soft technology evolution and the implications for solar energy cost trends
Not provided.
Energy & Fuels↗
Additive manufacturing of amorphous metal soft magnetic composites
Soft magnet alloys are used as magnetic cores for electric motors, transformers, wind turbines and other power generation systems. Soft magnetic cores are expensive and time consuming to manufacture in the complex shapes required for next-generation devices using conventional press and sinter powder metallurgy. The objective of this effort is to additively manufacture high performance soft magnets, with reduced cost and reduced material waste and 10x lower energy (core) loss at the high operating frequencies of many electric machines. Laser powder bed fusion additive manufacturing is used as the fabrication method. Electrical steel and amorphous alloys atomized powders are used as a feedstock materials. Magnetic cores are printed in topology optimized structures such as the Hilbert curve because this has been shown to reduce energy losses by minimizing the eddy currents that circulate within the magnet at high frequencies. In our project, we succeed in printing FeSi 3.5wt% and FeSi 6.5wt% electrical steels, and iron-based soft magnetic amorphous alloys in the shape of Hilbert and Peano curve topology optimized structures. We found that the Peano curve has a higher cut-off frequency than the Hilbert curve, and that amorphous alloys have high cut-off frequencies and higher mechanical hardness than electrical steels. Processing conditions such as laser power, scan speed, and hatching pattern were optimized to achieve high density prints, and optimize magnetic performance. We find that the printing of amorphous alloy soft magnetic cores may be technoeconomically feasible for large scale applications such as transformers, for which supply chain issues and the labor costs of manual fabrication of magnetic cores is prohibitive in some cases.
36 MATERIALS SCIENCE↗
Deep Reinforcement Learning for Microgrid Cost Optimization Considering Load Flexibility
This paper proposes a novel Soft-Actor-Critic (SAC) based Deep Reinforcement Learning (DRL) method for optimizing the cost of microgrid operation by leveraging load flexibility. The proposed SAC-DRL method is designed to coordinate the control of distributed energy resources (DERs) and flexible load, addressing practical energy billing formation by power distribution utilities. Key contributions include an innovative reward function to mitigate sparse reward challenges and a mixed control strategy for discrete and continuous variables, ensuring radial network topology and minimizing power loss. We evaluate the proposed method on the model of a real microgrid located in Southern California, U.S.. The SAC-DRL model is tested to demonstrate its efficacy in reducing grid dependence, optimizing resource use, and minimizing costs. The results highlight the potential of DRL in modern energy systems, offering a sustainable and economically efficient solution for energy management in microgrids.
deep reinforcement learning↗
Metal Halide Perovskite Solar Module Encapsulation Using Polyolefin Elastomers: The Role of Morphology in Preventing Delamination
The development of perovskite solar cells (PSCs) has ushered in a new era of solar technology, characterized by its exceptional efficiency and cost-effective production. However, the soft and fragile nature of perovskites makes module encapsulation challenging. Polyolefin elastomers (POEs) have been reported to be promising encapsulants for perovskite modules. However, little research exists on identifying criteria among different types of POEs as encapsulants. Here, two POEs with different morphologies were compared as encapsulants. The first POE crystallizes during encapsulation (crystal content ∼40%), and the resulting shrinkage or warpage leads to delamination, causing minimodule failure. In contrast, perovskite minimodules encapsulated with a mostly amorphous POE exhibited better reliability and reproducibility. The best perovskite minimodules passed the thermal cycling test for 240 cycles between −40 and 85 °C and the damp heat test for 1419 h, according to the IEC 61215 standard. This study highlights the importance of the morphology of encapsulants in achieving high-quality encapsulation. Published by the American Physical Society 2024
14 SOLAR ENERGY↗
Probing Accuracy-Speedup Tradeoff in Machine Learning Surrogates for Molecular Dynamics Simulations
The performance promise of machine learning surrogates of molecular dynamics simulations of soft materials is significant but generally comes at the cost of acquiring large training datasets to learn the complex relationships between input soft material attributes and output properties. Under the constraint of limited high-performance computing resources, optimizing the size of the training datasets becomes paramount. Using an artificial neural network based surrogate for molecular dynamics simulations of confined electrolytes, we explore the tradeoff between surrogate accuracy and computational gains. Accuracy is assessed by computing the root-mean-square errors between the surrogate predictions and the ground truth results obtained via molecular dynamics simulations. The computational performance is judged by evaluating the speedup which incorporates the training dataset creation time. Improvement in accuracy occurs with a loss of speedup, which scales as the inverse of the training dataset size. Furthermore, the link between surrogate generalizability and the accuracy-speedup tradeoff is assessed by examining the errors incurred in surrogate predictions on unseen, interpolated input variables and developing a net speedup metric to capture the associated gains.
Anions↗
Bottom-Up Soft Magnetic Composites
In order to meet 2025 goals for enhanced peak power (100 kW), specific power (50 kW/L), and reduced cost (3.3 $/kW) in a motor that can operate at ≥ 20,000 rpm, improved soft magnetic materials must be developed. Better performing soft magnetic materials will also enable electric motors without rare earth elements. In fact, replacement of permanent magnets with soft magnetic materials was highlighted in the Electrical and Electronics Technical Team (EETT) Roadmap as a R&D pathway for meeting 2025 targets. Eddy current losses in conventional soft magnetic materials, such as silicon steel, begin to significantly impact motor efficiency as rotational speed is increased. Soft magnetic composites (SMCs), which combine magnetic particles with an insulating matrix to boost electrical resistivity (ρ) and decrease eddy current losses, even at higher operating frequencies (or rotational speeds), are an attractive solution. Today, SMCs are being fabricated with values of ρ ranging between 10 -3 to 10 -1 μohm∙m, which is significantly higher than 3% silicon steel (~0.5 μohm∙m). The isotropic nature of SMCs is ideally suited for motors with 3D flux paths, such as axial flux motors. Additionally, the manufacturing cost of SMCs is low and they are highly amenable to advanced manufacturing and net-shaping into complex geometries, which further reduces manufacturing costs. There is still significant room for advancement in SMCs, and therefore additional improvements in electrical machine performance. For example, despite the inclusion of a non-magnetic insulating material, the electrical resistivities of SMCs are still far below that of soft ferrites (10 – 10 8 μohm∙m).
36 MATERIALS SCIENCE↗
Soft Actor Critic Based Volt-VAR Co-optimization in Active Distribution Grids
Modern distribution networks are undergoing several technical challenges, such as voltage fluctuations, because of high penetration of distributed energy resources (DERs). This paper proposes a deep reinforcement learning (DRL)-based Volt VAR co-optimization technique for reducing voltage fluctuations as well as power loss under high penetration of DERs. In addition, the proposed approach minimizes the operational cost of the grid. A stochastic policy optimization based soft actor critic (SAC) agent is proposed to configure the optimal set-points of the reactive power outputs of the inverters. The performance of the proposed model is verified on the modified IEEE 34- and 123-bus systems and compared with a base case scenario with no reactive supply by inverters, and a local droop control approach. The results demonstrate that the proposed framework outperforms the conventional droop control method in improving the voltage profile, minimizing the network power loss, and reducing grid operational cost.
—Distribution grids, deep reinforcement learning, ↗
Performance simulation of the soft gamma-ray concentrator
The soft gamma-ray concentrator is a telescope mission concept utilizing a suitable arrangement of bent multilayer structures of alternating low- and high-density materials. This lens is able to channel gamma-ray photons via total external reflection and concentrate the incident radiation to a point. The channeling technique offers the potential for concentrating gamma rays with focal lengths <10 m and energies >100 keV, beyond the reach of current grazing-incidence hard x-ray mirrors. For the performance estimation of such an instrument, we have developed a flexible set of computer modeling tools to compute the optical properties of multilayer structures, predict the channeling efficiency for a given multilayer configuration, and aid in the optimization of potential gamma-ray concentrator-based telescope designs. This modeling includes the multilayer optical properties calculated by the IMD software, the ray tracing using an IDL code, and the focal plane detector simulation by MEGAlib. We illustrate the potential of this approach by presenting simulated astronomical observations from a balloon-borne platform. The final result, including simulated effective area, instrument sensitivity, and polarization performance, shows that the gamma-ray concentrator will provide greatly increased sensitivity for next-generation soft gamma-ray missions with modest cost and complexity.
47 OTHER INSTRUMENTATION↗
Flexible Soft X-Ray Image Sensors based on Metal Halide Perovskites With High Quantum Efficiency
Soft X-ray imaging is a powerful tool to explore the structure of cells, probe material with nanometer resolution, and investigate the energetic phenomena in the universe. Conventional soft X-ray image sensors are by and large Si-based charge coupled devices that suffer from low frame rates, complex fabrication processes, mechanical inflexibility, and required cooling below -60 °C. Here, a soft X-ray photodiode is reported based on low-cost metal halide perovskite with comparable performance to commercial Si-based device. Nanothrough network electrode minimized the optical loss due to the shadowing of insensitive layers, while a multidimensional perovskite heterojunction is generated to reduce the photo-generated carrier loss. Further, this strategy promoted a record quantum efficiency of 8 × 10 3 % without cooling, several orders of magnitude greater than the previously achieved. Flexible and curved soft X-ray imaging arrays are fabricated based on this high-performance device structure, demonstrating stable soft X-ray response and sharp imaging capabilities. This work highlights the low-cost and efficient perovskite photodiode as a strong candidate for the next-generation soft X-ray image sensors.
36 MATERIALS SCIENCE↗
Electric Drive Technologies Research: Bottom-Up Soft Magnetic Composites (FY2022 Annual Progress Report)
In order to meet 2025 goals for enhanced peak power (100 kW), specific power (50 kW/L), and reduced cost (3.3 $\$$/kW) in a motor that can operate at ≥ 20,000 rpm, improved soft magnetic materials must be developed. Better performing soft magnetic materials will also enable rare earth free electric motors. In fact, replacement of permanent magnets with soft magnetic materials was highlighted in the Electrical and Electronics Technical Team (EETT) Roadmap as a R&D pathway for meeting 2025 targets. Eddy current losses in conventional soft magnetic materials, such as silicon steel, begin to significantly impact motor efficiency as rotational speed increases. Soft magnetic composites (SMCs), which combine magnetic particles with an insulating matrix to boost electrical resistivity (ρ) and decrease eddy current losses, even at higher operating frequencies (or rotational speeds), are an attractive solution. Today, SMCs are being fabricated with values of ρ ranging between 10 -3 to 10 -1 μohm∙m, which is significantly higher than 3% silicon steel (~0.05 μohm∙m). The isotropic nature of SMCs is ideally suited for motors with 3D flux paths, such as axial flux motors. Additionally, the manufacturing cost of SMCs is low and they are highly amenable to advanced manufacturing and net-shaping into complex geometries, which further reduces manufacturing costs. There is still significant room for advancement in SMCs, and therefore additional improvements in electrical machine performance. For example, despite the inclusion of a non-magnetic insulating material, the electrical resistivities of SMCs are still far below that of soft ferrites (10 – 10 8 μohm∙m).
33 ADVANCED PROPULSION SYSTEMS↗
Design, Modeling, and Testing of a Novel Inductor for Electric Vehicles: Iron Nitride Soft Magnetic Composites
New technology for electric vehicles (EVs) must meet the requirements of higher energy usage, lower costs, and more sustainable source materials. One promising material for EV power system components is iron nitride (IN) soft magnetic composites (SMCs) because of their competitive magnetic properties and high abundance of the source materials. As part of an ongoing program at Sandia National Laboratories, this project focused on using computer modeling to optimize the prototyping process for an iron nitride SMC toroidal inductor to reach a target inductance of 600 μH. Four inductors with different combinations of wiring (26 AWG and 20 AWG) and vol% loading of iron nitride (65 vol% and 50 vol%) were fabricated at Cal Poly and characterized using an LCR meter. These inductors were also modeled using COMSOL Multiphysics™ with the Magnetic Fields module. The inductance data from the experiment and the model show that the 65 vol% IN prototypes and models agree with about 8% difference, while the 50 vol% IN samples show about a 9% difference between the prototype and the model. These results suggest that the model can predict inductance with both accuracy and precision with low confidence for the given sample size of four. An additional parameter of AC resistance is studied but the AC resistance results from the inductors and from the model generally do not agree closely, suggesting that the current model used in the project does not fully capture the mechanisms behind AC resistance of the inductor. With the focus of the project on inductance, the percent difference results of less than 9% across the four inductors that were tested increases confidence in the model’s predictive capabilities for inductance only. Using the inductance results from both the model and experiment, the final suggested inductor design is a 65 vol% core with 150 windings of 20 AWG wire that is 8 cm across and 1.5 cm tall to reach the inductance goal of 600 μH based on analysis using the optimized COMSOLTM model.
33 ADVANCED PROPULSION SYSTEMS↗
Influence of minor Ce additions on magnetic and electrical properties in Fe–6.5 wt. % Si alloy
Fe–6.5 wt. % Si alloys exhibit lower core loss at higher frequency than the dominating electrical steel containing 3.2% Si due to its lower magneto-crystalline anisotropy energy (MAE) and higher electrical resistivity. However, compared to nanocrystalline and amorphous soft-magnetic materials, 6.5% Si steel, although being cost effective and having high saturation magnetization, still has much to improve, especially with respect to MAE and resistivity. To explore further improvement, the effects of minor Ce additions (due to its mixed valence 4f-electron configuration) on the microstructure, electrical and magnetic behaviors of Fe–6.5 wt. % Si alloys were investigated. Due to its limited solubility in Fe, Ce exhibits condition-dependent effects on electrical resistivity, showing only marginal improvement under rapid solidification. In contrast, Ce addition degrades magnetic performance, reducing saturation magnetization (from 1.86 to 1.76 T) and increasing coercivity (from 88 to 171 A/m), which results in an 83.4% increase in core loss (W 10/1000 condition). Overall, Ce addition provides limited benefit in electrical resistivity while deteriorating magnetic properties, making it ineffective for enhancing the soft magnetic performance of Fe–6.5 wt. % Si alloys.
36 MATERIALS SCIENCE↗
Laminated Permanent Magnets Enable Compact Magnetic Components in Current Source Converters
Magnetic components play a key role in determining the size, cost, efficiency of a power converter. They are more significant in current source converters, where the dc-link energy is stored in an inductor/transformer. In such cases, the magnetics have a predominantly DC flux, superimposed by an AC flux at switching frequency. Previously, permanent magnets have been proposed to offset the DC flux and thereby reduce the size, cost, and losses of the inductors/transformers. This paper starts by analyzing the issue of eddy currents when using such standard permanent magnets, especially in medium frequency applications, making them practically unusable. Alternatively, the paper proposes the use of laminated permanent magnets to reduce these eddy currents and thereby realize the benefits of offsetting the DC flux to reduce the size, cost, and loss of inductors/transformers. Using a soft-switching solid-state transformer as an example, experimental results are presented to show the impact of eddy currents in standard permanent magnets when used with a medium frequency transformer. Here, experimental results showing the benefits of using laminated magnets in reducing the eddy currents are presented.
30 DIRECT ENERGY CONVERSION↗
Benchmarking Soft Sensors for Remote Monitoring of On-Site Wastewater Treatment Plants
On-site wastewater treatment plants (OSTs) are usually unattended, so failures often remain undetected and lead to prolonged periods of reduced performance. To stabilize the performance of unattended plants, soft sensors could expose faults and failures to the operator. In a previous study, we developed soft sensors and showed that soft sensors with data from unmaintained physical sensors can be as accurate as soft sensors with data from maintained ones. The monitored variables were pH and dissolved oxygen (DO), and soft sensors were used to predict nitrification performance. In the present study, we use synthetic data and monitor three plants to test these soft sensors. We find that a long solids retention time and a moderate aeration rate improve the pH soft-sensor accuracy and that the aeration regime is the main operational parameter affecting the accuracy of the DO soft sensor. We demonstrate that integrated design of monitoring and control is necessary to achieve robustness when extrapolating from one OST to another in the absence of plant-specific fine-tuning. Additionally, we provide a unique labeled dataset for further feature and data-driven soft-sensor development. Our benchmarking results indicate that it is feasible to monitor OSTs with unmaintained sensors and without plant-specific tuning of the developed soft sensors. This is expected to drastically reduce monitoring costs for OST-based sanitation systems.
54 ENVIRONMENTAL SCIENCES↗