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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The environmental impact of hydropower: a systematic review of the ecological effects of sub-daily flow variability on riverine fish

Hydropower can help facilitate power grid decarbonization because it can respond to short-term changes in power demand and is comparatively more reliable than intermittent wind and solar. However, flexible hydropower operations can create rapid and abnormal fluctuations in downstream flow conditions, which can negatively impact aquatic ecosystems. Accordingly, we conducted a systematic review on the ecological effects of hydropower-driven sub-daily flow variability (SDFV) on riverine fishes. We reviewed and synthesized 109 articles relevant to fish-SDFV relationships from seven sources, most of which focused on Salmonids in North America and northern and western Europe and were published in the last 15 years. We found strong agreement in the literature that SDFV increases fish stranding risk, destabilizes habitat, and decreases production and diversity. We found moderate agreement that SDFV interrupts fish reproduction, increases or has no impact on condition, and prompts or discourages movement depending on local channel conditions. We found little to no agreement for relationships between SDFV and mortality, physiology, and behavior. The effects of SDFV on riverine fish ecology are intertwined in the complex suite of biotic and abiotic characteristics that structure aquatic ecosystems and are highly site-, species-, and life stage-specific. Assessments of the impact of SDFV on fish ecology should first characterize local habitat and channel quality and fish community composition to identify specific, measurable ecological outcomes to sustain or enhance, and then design mitigation strategies tailored to those ecological objectives.

13 HYDRO ENERGY↗
DOE OSTI2024

Solid State Reduction Driven Synthesis of Mn Containing Multi-principal Component Alloys

In recent years, high entropy alloys (HEAs), also known as multi-principal component alloys (MPCAs) have emerged as a new and exciting class of materials. This paper reports on the solid state reduction synthesis of a series of CoFeNiMn-based MPCA compositions, starting from a mixture of the corresponding oxides. One of the aims of the study was to test whether the degree of reduction of MnO, a highly stable oxide, could be enhanced by tailoring the alloy composition. Specifically, the influence of Ni content was studied because Ni exhibits a significant negative enthalpy of mixing with Mn. High purity precursor powders of Co(OH) 2 , Fe 2 O 3 , MnO 2 , and NiO were milled and mixed using standard ceramic processing methods. Here, the nominal sample compositions (assuming complete oxide reduction) were (CoFeMn) x Ni (1-x) , for x = 0, 0.083, 0.166, and 0.25. The oxide samples were subjected to a series of isothermal reduction anneals in flowing 3 pct H 2 –Ar at 1100 °C. The resulting microstructures were characterized using scanning electron microscopy (SEM), X-ray energy dispersive spectroscopy (EDS) and X-ray diffraction (XRD). The composition of the resulting MPCAs was determined quantitatively using wavelength dispersive spectroscopy (WDS) in the electron microprobe. The study revealed that for each of the initial oxide compositions studied, it was possible to achieve an MPCA with ~ 25 at. pct Mn. These results were found to be consistent with the predictions of a thermodynamic model whereby a negative enthalpy of mixing (ΔH mix ), combined with a contribution from configurational entropy, can offset a positive free energy of reduction (ΔG red ). The incorporation of vibrational entropy into first principles calculations was found to have a significant effect on the predicted crystal structure of the MPCAs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Influence of Solidification Velocity on Partitioning and Precipitate Evolution in a Multicomponent, Soft Magnetic Alloy Processed by Laser Melting

Multicomponent alloys have recently been shown to overcome the structural-functional barrier associated with soft magnetic materials. Here, single laser tracks have been used to investigate the effects of solidification velocity on the Al 7 Fe 32.6 Co 27.7 Ni 27.7 Ta 5 (at pct) alloy. Ta-rich cell wall partitioning was shown to affect the final distribution and size of the precipitating L1 2 phase, after aging. These findings demonstrate that solidification rate control offers the potential to tailor localized performance in alloys with size-dependent precipitation properties.

Brookins, Jeffrey M. [Oak Ridge National Laborator↗
DOE OSTI2026

Comparative Analysis of Austenitized Steel Microstructures Due to Induction and Conventional Heating in High Magnetic Field

While magnetic field-assisted processing shows promise in improving alloys’ mechanical properties, insufficient descriptions of heating methods hinder development of tailored processing pathways. This study investigates how induction and conventional heating affect microstructures of near-eutectoid Fe–C steel with and without 9 T magnetic field. Conventionally heated samples in field showed more austenite grain growth compared to inductively heated counterparts. Annealing twin frequency increased under field, with twin density increases not fully explained by larger grain sizes.

Hurley, Megan [University of Florida, Gainesville]↗
DOE OSTI2022

Emerging Opportunities in Manufacturing Bulk Soft-Magnetic Alloys for Energy Applications: A Review

Abstract Soft-magnetic alloys exhibit exceptional functional properties that are beneficial for a variety of electromagnetic applications. These alloys are conventionally manufactured into sheet or bar forms using well-established ingot metallurgy practices that involve hot- and cold-working steps. However, recent developments in process metallurgy have unlocked opportunities to directly produce bulk soft-magnetic alloys with improved, and often tailorable, structure–property relationships that are unachievable conventionally. The emergence of unconventional manufacturing routes for soft-magnetic alloys is largely motivated by the need to improve the energy efficiency of electromagnetic devices. In this review, literature that details emerging manufacturing approaches for soft-magnetic alloys is overviewed. This review covers (1) severe plastic deformation, (2) recent advances in melt spinning, (3) powder-based methods, and (4) additive manufacturing. These methods are discussed in comparison with conventional rolling and bar processing. Perspectives and recommended future research directions are also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Mechanical Behavior of Additively Manufactured Molybdenum and Fabrication of Microtextured Composites

Refractory metals are a class of high-melting-temperature materials suitable for use in extreme environment applications. Interestingly, during additive manufacturing many pure refractory metals exhibit a switch from (001) to (111) build direction fiber preference with increasing surface energy density. Here we exploit this solidification physics to fabricate material with “mesoscale composite” engineered structures consisting of features with contrasting (001) and (111) build direction microtextures. Separately, elevated temperature tensile testing of EBM fabricated material with a randomized distribution of mixed (001)/(111)-fiber grains is shown to exhibit excellent properties. These results are utilized to build a crystal plasticity model for evaluating the local inelastic response of the composite mesoscale structures. Analysis of printed microstructures and microstructure-scale simulations indicate that both macro-scale and localized material behavior may be tailored. This strategy can be potentially used to synthesize materials with optimized performance for high-temperature applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Powder Metallurgy HIP Process Study and Mechanical Property Evaluations for IN740H

The effects of hot isostatic pressing (HIP) parameters and powder size distribution (PSD) on the resulting microstructure and properties of powder metallurgy (P/M) IN740H have been investigated. Properties can be substantially tailored through the appropriate management of these parameters. Applying a thermal presoak to the input powder prior to the sintering stage of HIP can significantly increase average grain size and coarse grain fraction, enabling a trade for improved creep resistance at the expense of tensile strength. Here, powder thermal presoak was found to have a larger impact on microstructure and properties than the use of a coarser PSD alone. Additionally, substantial grain growth and grain boundary migration is observed in P/M IN740H at HIP temperatures above 1260°C. Given its inherently finer grain size, P/M HIP IN740H typically outperforms its cast and wrought counterparts in tensile response. Creep life at 700–800°C was inferior to wrought due to PPBs coincident with grain boundaries which served as cavity nucleation sites, leading to lower creep ductility. The ability to adjust the microstructure toward a more balanced creep and tensile behavior allows P/M HIP IN740H to be considered for high-temperature applications in advanced ultra-supercritical steam and supercritical carbon dioxide power cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Probing the Mechanical Performance of Micro-architected Porous Structures Through In Situ Characterization and Analysis

Micropores play critical roles in both natural and man-made materials. Such pores take on a variety of shapes and sizes ranging from spherical to irregular sphere-like voids with diameters spanning from the nanometer to millimeter scales. When porous structures are mechanically loaded, the pores direct the stress around their free surfaces, altering the material’s mechanical response relative to fully dense materials. In this study, for the first time, we create micro-architected porous samples using nanolithography and investigate the role of pore morphology by conducting a series of in situ micropillar compression tests in scanning electron microscopy. The findings demonstrate that porosity is the primary factor influencing the mechanical response of these micro-architected materials, as often seen on a macroscopic level. Additionally, we observed that pore geometry had a significant impact on Young’s modulus, yield stress, and strain energy density as a secondary parameter. Then, the extracted Young’s modulus was compared to macroscopic empirical models and determined the analytical models sufficiently described the impact of porosity in the microscopic scale but failed to capture the impact of second-order parameters. Furthermore, these results suggest how porous materials can be tailored to achieve desired mechanical properties based on the engineering applications of interest.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Hot-Roll Fabrication of Anisotropic Nanograin Nd-Fe-B Magnet

Nd-Fe-B based magnets have the highest energy product among all permanent magnets, which is required for numerous clean energy technologies. For higher temperature applications (T > 150°C), additions of heavy rare earth elements (HREEs) such as Dy are required to maintain sufficient coercivity during operation. Additions of Dy are expensive. Thus, it is desirable to reduce the need for HREEs by reducing the grain size to the nanoscale, which increases the coercivity and decreases its temperature dependence. Here, we report a novel nanograin Nd-Fe-B magnet fabrication method that is continuous and inexpensive. The process uses mechanically milled Nd-Fe-B melt-spun flakes as feedstock powder that is packed into a metal vessel and then hot rolled to form a fully dense and highly textured strip magnet with tailored thicknesses, down to 800 µm. Finally, using this process, fully dense nanograin bulk magnets can be synthesized in minutes compared to the traditional multi-step processes that are typically low throughput.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructural Evolution of Steel During Magnetic Field-Assisted Processing

Advancing magnetic field-assisted processing, as an energy-efficient method for tailoring steel microstructures, requires a thorough understanding of how the high magnetic field impacts microstructural evolution, particularly its effect on prior austenite grain structures. The current investigation of a near-eutectoid composition, Fe-C alloy, uses electron backscatter diffraction to examine the morphology and orientation of martensite and pearlite microstructures, and to reconstruct the parent austenite microstructures present during equivalent heating under varied magnetic field strengths (0-T, 2-T, 5-T, and 9-T). It was observed that the magnetic field has a negligible effect on martensite lath/block width, slightly decreases prior austenite grain size, and increases the fraction of austenite grains with annealing twins. Additionally, the magnetic field increases the phase fraction of proeutectoid ferrite but has a negligible effect on pearlite block size and the distribution of boundary misorientation angles. No preferred texture was induced by the magnetic field, regardless of the applied field direction, in the proeutectoid ferrite phase or the martensite and prior austenite microstructures. Furthermore, the observed results contradict previous literature, and the differences are discussed.

Magnetic Materials↗
DOE OSTI2025

Creep Deformation and Damage Mechanisms in an Advanced High-Temperature Additively Manufactured Nickel-Base Superalloy

Abstract This research investigates the processing–structure–properties–performance relationship in a novel nickel-base superalloy, ABD ® -900AM, designed for extreme environments. Specifically tailored for additive manufacturing (AM), ABD ® -900AM maintains mechanical integrity at high temperatures and is comparable to other nickel-based superalloys with a 30–40% gamma-prime volume fraction. A comprehensive study was conducted using laser-beam powder bed fusion and electron-beam powder bed fusion methods. Factors such as heat treatment, porosity, build orientation, and hot isostatic pressing were evaluated to understand their effects on microstructure and mechanical performance. Microstructural characterization revealed significant differences in grain size and orientation across build processes and heat treatments. High-temperature mechanical testing indicated that grain size, heat treatment, and orientation significantly influence creep behavior. A super-solvus heat treatment led to recrystallization and grain growth, significantly improving creep properties compared to a near-solvus heat treatment. Various creep mechanisms were identified across different conditions, and creep rupture models were developed for each build process. Post-test microstructural analysis showed grain boundary damage, with differences in creep cavitation morphology under varying stress conditions. It was shown that MC carbides grow at the expense of gamma-prime near grain boundaries, leading to precipitate-free zones in specimens tested at higher temperatures. This study fills a significant gap in fundamental research by offering a deeper insight into the high-temperature mechanical behavior of additively manufactured nickel-base superalloys. It also explores critical research questions regarding the role of carbides and the significance of heat treatment. The insights gained enhance confidence in the industry adoption of ABD ® -900AM and similar alloys for high-temperature applications, bridging the knowledge gap and supporting the development of reliable AM processes for extreme environments.

Bridges, Alex (ORCID:000000030338759X)↗
DOE OSTI2026

Deacetylation and Mechanical Refining Pathway for the Bioconversion of Sugarcane Bagasse

Advancing lignocellulose biorefining is imperative for the deployment of cellulosic (2G) biofuels. This work investigates the tailoring of the alkaline deacetylation and mechanical refining (DMR) pathway for the bioconversion of sugarcane bagasse. Experiments are conducted at laboratory and pilot scales, varying the pretreatment conditions (70–92 °C; 48–100 g NaOH /kg) and the mechanical refining technologies (PFI and disk refining). The pretreatments selectively solubilize acetyl groups (> 86%) and lignin (10–63%) while mostly preserving structural carbohydrates in the solid phase. Enzymatic hydrolysis generates hydrolysates of clean sugars (glucose and xylose), with sugar yields increasing up to 81% for glucose and 89% for xylose in response to delignification and mechanical refining. Biochemical methane potential assays reveal specific methane productions of up to 568 NmL CH₄ gVS⁻¹ for alkaline liquor monodigestion and 344 NmL CH₄ gVS⁻¹ for co-digestion with sugarcane vinasse from the conventional (1G) sugarcane ethanol, indicating a strong potential for bioenergy recovery from this process stream. Synergies are identified in integrating 1G ethanol, 2G DMR processing of bagasse, and anaerobic co-digestion of 1G vinasse and 2G DMR alkaline liquor. This technology enables sugarcane biorefineries to enhance the co-production of ethanol, methane, and concentrated streams of CO 2 .

09 BIOMASS FUELS↗
DOE OSTI2024

Aviation security screening optimizer for risk and throughput (ASSORT)

The increasing number of air travelers each year presents a challenge as many airports are near their capacity in terms of resources and space for passenger screening. Fortunately, advancements in technologies like next-generation millimeter wave scanning offer solutions to ease this strain. The focus remains on managing risk while enhancing the passenger experience for the traveling public. The risk model presented in this paper known as the Aviation Security Screening Optimizer for Risk and Throughput (ASSORT) is designed to assess risk-based approaches for passenger screening and checkpoint operations. Additionally, ASSORT is exploring various traveler categories — general, trusted, and trusted-plus — along with different checkpoint screening Concept of Operations tailored to each traveler type. For instance, travelers with a higher trust level may experience fewer screening technologies, resulting in quicker processing times at the checkpoint. The output of ASSORT provides a risk score for predefined threat scenarios, as well as the overall risk to the checkpoint, aircraft, and airport by traveler type. In conclusion, benefits of using this tool include assessing the trade-offs between the overall risk associated with checkpoints and the throughput rate of passengers screened. We show for example the impact that different passenger volumes at the checkpoint can have on risk.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Generation and representation of synthetic smart meter data

Advanced energy algorithms running at big-data scale will be necessary to identify, realize, and verify energy savings to meet government and utility goals of building energy efficiency. Any algorithm must be well characterized and validated before it is trusted to run at these scales. Smart meter data from real buildings will ultimately be required for the development, testing, and validation of these energy algorithms and processes. However, for initial development and testing, smart meter data are difficult to work with due to privacy restrictions, noise from unknown sources, data accessibility, and other concerns which can complicate algorithm development and validation. This paper describes a new methodology to generate synthetic smart meter data of electricity use in buildings using detailed building energy modeling, which aims to capture the variability and stochastics of real energy use in buildings. The methodology can create datasets tailored to represent specific scenarios with known truth and controllable amounts of synthetic noise. Knowledge of ground truth also allows the development and validation of enhanced processes which leverage building metadata, such as building type or size (floor area), in addition to smart meter data. The methodology described in this paper includes the key influencing factors of real-world building energy use including weather data, occupant-driven loads, building operation and maintenance practices, and special events. Data formats to support workflows leveraging both synthetic meter data and associated metadata are proposed and discussed. Finally, example use cases of the synthetic meter data are described to illustrate potential applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Heterogeneities at multiple length scales in 2D layered materials: From localized defects and dopants to mesoscopic heterostructures

Two-dimensional (2D) materials hold great promise for applications in optoelectronics, quantum information science, and energy conversion due to their remarkable properties imbued by their physical characteristics. Although heterogeneities in their intrinsic structure are the major challenges limiting their synthesis and predictable properties, they also provide a pathway to controllably tune the properties and broaden the potential of 2D materials. Heterogeneities that can be tailored, including defects, dopants, strain, edges, and layer stackings offer transformative opportunities in heterogeneous 2D materials through the introduction of novel properties for technological applications. This article provides a review of recent progress in studying heterogeneities in 2D materials. The review uses examples from our work to develop a strategy to understand the heterogeneities across multiple length scales to link the effect of heterogeneity at the nanoscale with the macroscale properties of 2D materials. We describe specific types of heterogeneities and explore novel synthesis and processing methods for their controlled production with example of the potential impact and applications enabled by their intriguing properties. Finally, we provide a perspective on how to extend the range of tunable properties through further engineering the heterogeneities in 2D materials.

2D materials↗
DOE OSTI2020

3D printing of metal-based materials for renewable energy applications

Large-scale renewable energy must overcome conversion and storage challenges before it can replace fossil fuels due to its intermittent nature. However, current sustainable energy devices still suffer from high cost, low efficiency, and poor service life problems. Recently, porous metal-based materials have been widely used as desirable cross-functional platforms for electrochemical and photochemical energy systems for their unique electrical conductivity, catalytic activity, and chemical stability. To tailor the porosity length scale, ordering, and compositions, 3D printing has been applied as a disruptive manufacturing revolution to create complex architected components by directly joining sequential layers into designed structures. In conclusion, this article intends to summarize cutting-edge advances of metal-based materials for renewable energy devices (e.g., fuel cells, solar cells, supercapacitors, and batteries) over the past decade.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predictive models enhance feedstock quality of corn stover via air classification

Feedstock heterogeneity is a fundamental obstacle to cost-competitive biobased products. Agricultural products like corn stover have anatomical components that vary in their chemical composition, mechanical properties, structure, and response to chemical and biological treatments. A technique that can enrich streams in select anatomical fractions would allow a tailored deconstruction approach to increase overall process efficiency. Air classification can be leveraged for such refining, however, fundamental characterization and understanding of the particle properties that underly the physics of air classification are only modestly documented. Here, we determine fundamental particle properties including mass-to-area ratio, drag coefficient, and partition velocity that describe how anatomical tissues of corn stover behave during air classification. In this work, mass-to-area ratios of anatomical tissues vary by nearly two orders of magnitude from 2.3 mg/mm 2 for cob to 0.04 mg/mm 2 for leaf. Drag coefficients of longer, fibrous materials (i.e., rind, husk, and sheath) are shown to correlate with particle area (p-value < 0.001) whereas granular tissues (i.e., cob, pith, and leaf) correlate better with mass-to-area ratio (p-values < 0.001). When compared to experimental observations, a simulated two-stage air classification and size reduction scenario predicts the overall partitioning of anatomical tissues within 15% for pith, husk, rind, and cob tissues. The model predicts an air-classified fraction preferentially enriched in cob (purity = 20%), rind (purity = 74%), and pith (purity = 4.5%) with a mass yield of 47%. Empirical relations for these properties can be used to predict the partitioning of corn stover during air classification based on anatomical type and size.

09 BIOMASS FUELS↗