Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “improvements”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Improving dairy manure hydrolysis and acidification through microbial community restructuring by adaptation to hyperthermophilic conditions

Dairy manure (DM) contributes significantly to greenhouse gas emissions and ecosystem degradation, yet its resistance to biodegradation hinders widespread bioprocessing applications. Lignocellulosic materials in DM pose a particular challenge because of their recalcitrance. Bioprocessing under hyperthermophilic (≥70 °C) conditions potentially offers an advantage over traditional fermentation temperatures due to enhanced activity of enzymes and the kinetics of enzymatic reactions. This can lead to a higher conversion rate and a greater extent of biomass hydrolysis and acidification. To test the validity of this hypothesis, the current study evaluated the efficacy of anaerobic hydrolysis and acidogenic fermentation of DM under mesophilic, thermophilic, and hyperthermophilic conditions. All inocula were adapted to corresponding temperatures but were derived from the same mesophilic source. Hyperthermophilic conditions resulted in superior DM hydrolysis efficiency (53%) compared to mesophilic (34%) and thermophilic (42%) conditions. The hyperthermophilic environment was particularly favorable to the decomposition of crude proteins and hemicellulose, which were reduced by 64% and 54%, respectively. Furthermore, hyperthermophilic fermentation also yielded the highest volatile fatty acid (VFA) production rate of 460 mg/L/day during the first four days, representing improvements of 50% and 90% over mesophilic and thermophilic conditions. In part, this was attributed to the enhanced production of branched-chain VFAs, including an increase of 6–10% in isobutyric acid and 12–13% in isovaleric acid. At hyperthermophilic conditions, however, there was no accumulation of VFAs during the days 5–8 of fermentation, which could be due to acetate conversion by the syntrophic acetate-oxidizing bacteria. A considerable gain in hydrolysis efficiency and VFA production rate were accompanied by a reduction in microbial diversity, which suggests that hyperthermophilic temperature is a favorable environment for the selection of organisms with enhanced DM hydrolysis and fermentation capabilities. A significantly increased relative abundance of xylanolytic Caldicoprobacter (23% of population) and proteolytic Thermovirga (9% of population) could be the major contributors to improved decomposition of hemicellulose and protein. As revealed by the techno-economic analysis, acidogenic fermentation of DM at 70 °C and a retention period of 4 days provides the greatest positive net present value, highest internal rate of return of 9.2%, and shortest investment payback period of 9 years. Furthermore, this study demonstrates that hyperthermophilic conditions enable superior deconstruction and bioconversion of lignocellulose-containing biomass into VFAs under reduced retention times, offering a promising approach for improving DM management and generating bioproducts.

09 BIOMASS FUELS↗
DOE OSTI2023

Processing aluminum alloy with hybrid wire arc additive manufacturing and ultrasonic nanocrystalline surface modification to improve porosity, surface finish, and hardness

Here, in this paper, a novel hybrid wire arc additive manufacturing (WAAM) and ultrasonic nanocrystal surface modification (UNSM) on porosity manipulation and surface properties of aluminum 5356 alloys was studied. The goal is to improve the quality of the WAAM-built part by eliminating bigger pores and reducing its size, reducing surface roughness, and increasing surface hardness. The as-built WAAM and WAAM-UNSM-treated samples were quantitatively studied for porosity using an X-ray micro-computed tomography (μ-CT). The surface roughness was measured on the surface profile of the same samples before and after UNSM treatment. Followed by the Vickers micro-hardness tests to evaluate the hardness modified by the influence of the UNSM treatment. It was found that the bigger pores in the as-built WAAM samples were eliminated and the medium-sized pores were shrunk to almost half the size after the UNSM treatment. Further, the UNSM treatment showed a significant improvement in both surface roughness and hardness on the WAAM Al5356 samples. This experimental work demonstrates the critical advantages of hybrid WAAM-UNSM in improving the qualities of the WAAM processed parts.

Hardness↗
DOE OSTI2023

Improving the barrier properties of tin oxide in metal halide perovskite solar cells using ozone to enhance nucleation

Here we investigate tin oxide growth on fullerene (C 60 ) by atomic layer deposition (ALD) for C 60 /oxide bilayer electron selective contacts in P-I-N metal halide perovskite (MHP) solar cells. An in situ ozone functionalization step is incorporated in an ALD SnO x process to suppress sub-surface growth, leading to improved internal barrier performance of ALD SnO x thin films grown on fullerene surfaces. We show that this approach decreases the water-vapor transmission rate of C 60 /ALD SnO x barriers by an order of magnitude and improves the barrier properties against gas, solvent, and halide migration. Furthermore, ozone-treated SnO x barriers can narrow photovoltaic performance distribution without compromising efficiency. We demonstrate the universality of this approach in wide-, intermediate-, and low-gap perovskite systems and further show that enhancement of the ALD barrier layer is critical toward improving the yield of all-perovskite tandem solar cells. Two-terminal all-perovskite tandem solar cells incorporating ozone nucleation are reported at over 24% photovoltaic conversion efficiency.

14 SOLAR ENERGY↗
DOE OSTI2022

Dynamic machine learning-based optimization algorithm to improve boiler efficiency

With decreasing computational costs, improvement in algorithms, and the aggregation of large industrial and commercial datasets, machine learning is becoming a ubiquitous tool for process and business innovations. Machine learning is still lacking applications in the field of dynamic optimization for real-time control. This work presents a novel framework for performing constrained dynamic optimization using a recurrent neural network model combined with a metaheuristic optimizer. The framework is designed to augment an existing control system and is purely data-driven, like most industrial Model Predictive Control applications. Several recurrent neural network models are compared as well as several metaheuristic optimizers. Hyperparameters and optimizer parameters are tuned with parameter sweeps, and the resulting values are reported. Further, the best parameters for each optimizer and model combination are demonstrated in closed-loop control of a dynamic simulation, and several recommendations are made for generalizing this framework to other systems. Up to 0.953% improvement is realized over the non-optimized case for a simulated coal-fired boiler. While this is not a large improvement in percentage, the total economic impact is $991,000 per year, and this study builds a foundation for future machine learning with dynamic optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Improving adhesive bonding of short carbon fiber thermoplastic composites to aluminum alloys with a hybrid laser-plasma surface modification strategy

This study investigates hybrid laser–plasma surface modification strategies for metal–CFRTP (carbon-fiber-reinforced thermoplastic polymer) dissimilar joints to improve their bonding performance, in contrast to existing literature that mostly focuses on either plasma or laser treatment alone. By conducting double cantilever beam (DCB) tests on adhesively-bonded AA5052 and CFRPA66 (carbon-fiber-reinforced polyamide 66) joints, as an example of metal–CFRTP joints, it was found that laser engraving on the metal surface combined with plasma treatment on the CFRTP surface significantly improved the specific fracture energy of the joint by 187% and 31% compared to as-received and plasma-treated-only joints, respectively. However, the hybrid treatment of laser engraving and plasma on the investigated CFRTP surface did not improve the bonding performance of the joints. The underlying mechanisms related to hybrid laser-plasma surface modification strategies were further investigated by examining the surface and cross-sectional morphologies after DCB testing using microscopy. Computational modeling was performed to elucidate the interaction between grooves on the metal substrate and the CFRTP–adhesive interfacial bonding in metal–CFRTP joints. This study provides new insights into developing surface modification methods for achieving strong metal–CFRTP adhesive joints, aimed at lightweighting structural components in automotive, aerospace, and other applications.

Adhesive bonding↗
DOE OSTI2020

Homogeneous titanium hydroxide yields hydroxyl decorated nanofiber separator with improved electrolyte uptake and device capacitance

Good wettability of separators in batteries and capacitors is crucial for performance. Titanium hydroxide (Ti(OH) x ) has been shown to greatly improve the wettability of polyvinylidene fluoride (PVDF) nanofibers (NFs). Here, PVDF/Ti(OH) x NFs were evaluated for their potential use as separators. Results show that PVDF NFs incorporated with Ti(OH) x have improved electrolyte uptake (EU) between 550 and 1200 EU% compared to untreated PVDF with less than 100 EU%. Significant improvements in capacitance are seen from capacitors utilizing PVDF/Ti(OH) x NF separators with the best performing PVDF/Ti(OH) x NF at 15 wt% Ti(OH)x with 446.8 mF/cm 2 compared to untreated PVDF NF separator with 262.7 mF/cm 2 .

36 MATERIALS SCIENCE↗
DOE OSTI2020

Maximizing the grafting of zwitterions onto the surface of ultrafiltration membranes to improve antifouling properties

Superhydrophilic zwitterions have been extensively exploited for surface modification to improve antifouling properties. However, it remains challenging to form layers of <20 nm with high zwitterion content on the surfaces with different degrees of hydrophilicity. We demonstrate that amine-functionalized sulfobetaine (SBAm) can be co-deposited with dopamine on ultrafiltration (UF) membranes, leading to a thickness of 10 nm to 50 nm and an SBAm content of up to 31 mass% in the coating layers. The covalently grafted SBAm is stable underwater and improves the antifouling properties, as evidenced by the lower trans-membrane pressure required to retain targeted water fluxes than that required for the pristine membranes. Finally, the SBAm is also more effective than conventionally used sulfobetaine methacrylate (SBMA) for the zwitterion grafting on the surface to improve antifouling properties

42 ENGINEERING↗
DOE OSTI2023

Minor titanium addition markedly improves the co-deformability of copper-tantalum composites

We investigated the microstructures of equal channel angular extrusion (ECAE) processed sintered composites of copper (Cu) and tantalum (Ta). These composites were produced with nominal Ta volume percentages ranging from 25% to 75%. Additional composites of the same nominal Ta content were prepared with a minor amount of titanium (Ti) added to facilitate interphase bonding. Ti addition was found to markedly improve the strength and co-deformability of the composites, allowing reliable extrusion of Cu-Ta composites with novel microstructures. A major influence of the Ti appears to be to soften the Ta, which contributes to improved co-deformability by reducing the flow stress mismatch between the composite constituents. Here, a change in the fracture surface morphology in the Ti-modified composite compared to the unmodified composite suggests that the Ti may also improve cohesion between Cu and Ta by reducing Ta surface oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Recent improvements in beam orbit feedback at NSLS-II

This paper discusses improvements made to the fast orbit feedback (FOFB) system at NSLS-II in 2019. We first characterized the system by measuring the transfer function and the closed-loop gain of the system. We also constructed a model to predict the performance, which was based on physical behaviors. After finding and fixing the sources of the latency, we improved the closed-loop gain’s bandwidth from 250 Hz to 400 Hz in the horizontal plane and 250 Hz to 300 Hz in the vertical plane. As a result, the orbit beam instability improved by 30% and 10% in the horizontal and vertical planes, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Improvement in the spatial resolution for imaging with fast neutrons

In this paper, we report on the realization of an improved concept for the detection of fast neutrons using a specific setup of scintillators with the aim to overcome limitations in spatial resolution. While fast neutron imaging (FNI) is a technique to investigate very thick material layers in transmission mode, currently there is a limit in the spatial resolution at ~1 mm, which hinders the performance for the detection of features like cracks, material damage or pores in large objects ($\gg$1 cm 3 ). The improved concept presented here is based on the separation of the converter material from the scintillation layer in a suitable way, which was successfully tested and referenced to the standard devices at the NECTAR facility (FRM-2, MLZ, Garching) under realistic beamline conditions. The results imply a break-through in enabling detection of structures on the order of 0.2 to 0.5 mm (a more precise estimation is not possible due to other resolution determing factors in a realistic setup). Potential additional improvements are discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Improvement of the AC orbit bump method of local kick factor measurement using reference bunches

Here we have improved the AC orbit bump technique of local kick factor measurement. This technique, developed at NSLS-II a few years ago, is based on local sine-wave excitation of the electron beam by four fast correctors adjacent to the impedance location. By implementing reference bunches and the time gate function of beam position monitors, we are now able to measure the AC orbits of bunches with different intensities simultaneously. This improvement allows us to obtain the impedance-driven orbit distortion in a single measurement, significantly improving the measurement accuracy and reducing the measurement time.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An improved pressure drop correlation for modeling localized effects in a pebble bed reactor

Advances in the development of pebble bed reactors (PBRs) has created a desire for accurate and cost-effective simulation tools for design scoping studies and safety analysis. The current state-of-the-art for these simulations is the use of porous media models, although these models rely on correlations to capture the effects of flow features that are not explicitly modeled. One of the areas where correlation accuracy is currently lacking is in the near-wall region of the bed. In this region, the presence of the wall causes the pebbles to pack more orderly, drastically changing the geometry and flow behavior in this region. This work presents a new generalized pressure drop correlation for PBRs based on the KTA equation. A high-to-low methodology is applied, where large eddy simulation (LES) is performed on two beds of 1568 and 1700 pebbles to generate a high-fidelity dataset. The flow fields are then averaged in time and separated into concentric rings of 0.05 D peb width. Average porosity, velocity, and pressure drop are extracted for each ring and the friction and form losses are calculated. The Reynolds number range for this study is 625–10,000, and thus the form losses are dominant over the friction losses. The form losses across the rings are investigated, and a correction term for the form loss calculation is determined and applied to the KTA equation to drastically improve the capability of modeling localized porosity effects in a porous media code. Finally, the improved correlation reduces near-wall velocity prediction error from over 50% with the KTA correlation to around 5%. Agreement in pressure drop prediction between LES and porous media simulations is also improved.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

An investigation on machine learning predictive accuracy improvement and uncertainty reduction using VAE-based data augmentation

The confluence of ultrafast computers with large memory, rapid progress in Machine Learning (ML) algorithms, and the availability of large datasets place multiple engineering fields at the threshold of dramatic progress. However, a unique challenge in nuclear engineering is data scarcity because experimentation on nuclear systems is usually more expensive and time-consuming than most other disciplines. One potential way to resolve the data scarcity issue is deep generative learning, which uses certain ML models to learn the underlying distribution of existing data and generate synthetic samples that resemble the real data. In this way, one can significantly expand the dataset to train more accurate predictive ML models. In this study, our objective is to evaluate the effectiveness of data augmentation using variational autoencoder (VAE)-based deep generative models. We investigated whether the data augmentation leads to improved accuracy in the predictions of a deep neural network (DNN) model trained using the augmented data. Additionally, the DNN prediction uncertainties are quantified using Bayesian Neural Networks (BNN) and conformal prediction (CP) to assess the impact on predictive uncertainty reduction. To test the proposed methodology, we used TRACE simulations of steady-state void fraction data based on the NUPEC Boiling Water Reactor Full-size Fine-mesh Bundle Test (BFBT) benchmark. Here, we found that augmenting the training dataset using VAEs has improved the DNN model’s predictive accuracy, improved the prediction confidence intervals, and reduced the prediction uncertainties.

Bayesian neural network↗
DOE OSTI2023

Medium-temperature phase change material integration in domestic heat pump water heaters for improved thermal energy storage

In this review, we examine state-of-the-art developments in integrating phase change materials (PCMs) for thermal energy storage (TES) in domestic heat pump water heaters (HPWHs). The component design optimization and control optimization of HPWHs and TES are reviewed for insight into improving the thermal capacity and efficiency of a PCM-integrated HPWH. The state-of-the-art review is categorized by the stage of development of the PCM for deployment in HPWHs. To select appropriate PCMs for HPWHs, a six-factor down-selection process is used to determine the best material(s) for integration in HPWHs with appropriate heat exchanger design. Ultimately, food-grade PCMs appear to be the best candidate for integration of TES in domestic HPWHs because they are nontoxic, highly cyclable, and have heat transfer properties accommodable to water heating when integrated in a manner to overcome the thermal conductivity limitations of the material. A key parameter of water heating performance is thermal heating power of the PCM component, which is not often reported. Many studies report significant improvement in capacity and efficiency. Furthermore, many performance metrics are identified from the literature to quantify the system performance, but agreement across studies is not found. Unified energy factor and first hour rating performance tests are required for commercially available HPWHs, and these performance metrics could homogenize the literature. Ultimately, we find that select PCMs are ready for deployment with HPWHs for performance improvement, and component design and control optimizations are new avenues of research and development required for a commercially viable PCM-integrated HPWH system.

25 ENERGY STORAGE↗
DOE OSTI2022

Physics-guided machine learning for improved accuracy of the National Solar Radiation Database

The National Solar Radiation Database (NSRDB) provides high-resolution spatiotemporal solar irradiance data for the entire globe. The NSRDB uses a two-step Physical Solar Model (PSM) to compute the effects of clouds and other atmospheric variables on the solar radiation reaching the surface of the Earth. Physical and optical cloud properties are fundamental inputs to the PSM and are derived from the National Oceanic and Atmospheric Administration's Geostationary Operational Environmental Satellites. This paper describes recent improvements to the NSRDB driven by physics-guided machine learning methods for cloud property retrieval. The impacts of these new methods on the NSRDB irradiance data are validated using an extensive set of ground measurement sites, showing significant improvement for all sites. We report on average, the mean absolute percentage error for global horizontal irradiance and direct normal irradiance show reductions of 2.16 and 3.95 percentage points respectively for all daylight conditions, 5.92 and 17.39 percentage points respectively for cloudy conditions, and 9.00 and 22.59 percentage points respectively for gap-filled cloudy conditions. These new methods will help improve the quality and accuracy of the irradiance and cloud data in the NSRDB.

14 SOLAR ENERGY↗
DOE OSTI2021

Improving the performance of first- and last-mile mobility services through transit coordination, real-time demand prediction, advanced reservations, and trip prioritization

Socio-demographic trends and recent economic development patterns have resulted in travel behavior changes that call for more flexible and accessible public transit options. Because flexible transit services vary in scope, size, and service type, new data-informed methods are useful to optimize services based on the specific needs of local communities and riders. In this study, real-world demand and vehicle trajectory data were used to evaluate and optimize system performance for an existing first-mile–last-mile (FMLM) service in Robinson Township, PA. A general FMLM model for arbitrary demand and service supply was then developed to quantify system performance—both travel time costs and day-to-day reliability—for various operational polices considering spatio-temporal demand variation and transportation network dynamics. Heuristics were used for optimal real-time vehicle routing in sizable real-world networks accommodating various service types and scopes. In this case study, total user costs were reduced by 18.6% when rides were coordinated with mainline fixed-route transit. Predictive routing strategies were shown to marginally improve system performance under sparse and variable spatio-temporal demand. The case study also highlights potentially large travel time and user reliability improvements—reductions of 51% and 53.8%, respectively—when trip requests were made in advance of their desired pickup time. Finally, we show that travel time reliability can be improved for time-inflexible trips with trip prioritization without increasing total user costs. These results were stable to changes in demand density.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Dynamic control over feedback regulatory mechanisms improves NADPH flux and xylitol biosynthesis in engineered E. coli

We report improved NADPH flux and xylitol biosynthesis in engineered E. coli. Xylitol is produced from xylose via an NADPH dependent reductase. We utilize 2-stage dynamic metabolic control to compare two approaches to optimize xylitol biosynthesis, a stoichiometric approach, wherein competitive fluxes are decreased, and a regulatory approach wherein the levels of key regulatory metabolites are reduced. The stoichiometric and regulatory approaches lead to a 20-fold and 90-fold improvement in xylitol production, respectively. Strains with reduced levels of enoyl-ACP reductase and glucose-6-phosphate dehydrogenase, led to altered metabolite pools resulting in the activation of the membrane bound transhydrogenase and an NADPH generation pathway, consisting of pyruvate ferredoxin oxidoreductase coupled with NADPH dependent ferredoxin reductase, leading to increased NADPH fluxes, despite a reduction in NADPH pools. Furthermore, these strains produced titers of 200 g/L of xylitol from xylose at 86% of theoretical yield in instrumented bioreactors. We expect dynamic control over the regulation of the membrane bound transhydrogenase as well as NADPH production through pyruvate ferredoxin oxidoreductase to broadly enable improved NADPH dependent bioconversions or production via NADPH dependent metabolic pathways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Introduction of NADH-dependent nitrate assimilation in Synechococcus sp. PCC 7002 improves photosynthetic production of 2-methyl-1-butanol and isobutanol

Cyanobacteria hold promise for renewable chemical production due to their photosynthetic nature, but engineered strains frequently display poor production characteristics. These difficulties likely arise in part due to the distinctive photoautotrophic metabolism of cyanobacteria. Here, in this work, we apply a genome-scale metabolic model of the cyanobacteria Synechococus sp. PCC 7002 to identify strain designs accounting for this unique metabolism that are predicted to improve the production of various biofuel alcohols (e.g. 2-methyl-1-butanol, isobutanol, and 1-butanol) synthesized via an engineered biosynthesis pathway. Using the model, we identify that the introduction of a large, non-native NADH-demand into PCC 7002's metabolic network is predicted to enhance production of these alcohols by promoting NADH-generating reactions upstream of the production pathways. To test this, we construct strains of PCC 7002 that utilize a heterologous, NADH-dependent nitrite reductase in place of the native, ferredoxin-dependent enzyme to create an NADH-demand in the cells when grown on nitrate-containing media. We find that photosynthetic production of both isobutanol and 2-methyl-1-butanol is significantly improved in the engineered strain background relative to that in a wild-type background. We additionally identify that the use of high-nutrient media leads to a substantial prolongment of the production curve in our alcohol production strains. The metabolic engineering strategy identified and tested in this work presents a novel approach to engineer cyanobacterial production strains that takes advantage of a unique aspect of their metabolism and serves as a basis on which to further develop strains with improved production of these alcohols and related products.

2-methyl-1-butanol↗