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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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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Optimizing first- and last-mile public transit services leveraging transportation network companies (TNC)

First-mile last-mile (FMLM) mobility services that connect riders to public transit can lead to improved transit accessibility and network efficiency if such services are convenient and reliable. However, many current FMLM services are inefficient and costly because they are inflexible (e.g., fixed supply of shuttles) and do not leverage collected data for optimized decision making. At the same time, new forms of shared mobility can provide added flexibility and real-time analytics to FMLM systems when carefully integrated. This study evaluates performance and cost implications of public/private coordination between transit shuttles and transportation network companies (TNC) in the FMLM context. A real-time operations model was developed to simulate daily operations for an existing FMLM system using real-world demand data. Three supply strategies were tested with varying levels of flexibility: (1) Status Quo (two 23-passenger on-demand shuttles), (2) Hybrid (one 23-passenger on-demand shuttle + TNC), and (3) TNC Only (exclusively use TNC services). Results indicated that the added flexibility of the Hybrid service design (using shuttles and TNCs) improved service performance (a 7.7% improvement), reduced daily operating costs (– 6.0%), and improved service reliability (95th percentile travel times decreased by up to 40% during peak periods). In addition, the Hybrid service design was more robust to variations in demand. Here, the Hybrid service was significantly cheaper to operate (– 31.6%) at reduced demand levels (50% of normal), and improved service performance (a 10.2% improvement) when demand levels were increased (150% of normal). These findings emphasize the importance of flexibility in FMLM service designs, especially when demand is sparse and variable.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Design of Bcl-2 and Bcl-xL Inhibitors with Subnanomolar Binding Affinities Based upon a New Scaffold

Employing a structure-based strategy, we have designed a new class of potent small-molecule inhibitors of the anti-apoptotic proteins Bcl-2 and Bcl-xL. An initial lead compound with a new scaffold was designed based upon the crystal structure of Bcl-xL and U.S. Food and Drug Administration (FDA) approved drugs and was found to have an affinity of 100 {micro}M for both Bcl-2 and Bcl-xL. Linking this weak lead to another weak-affinity fragment derived from Abbott's ABT-737 led to an improvement of the binding affinity by a factor of >10,000. Further optimization ultimately yielded compounds with subnanomolar binding affinities for both Bcl-2 and Bcl-xL and potent cellular activity. The best compound (21) binds to Bcl-xL and Bcl-2 with K{sub i} < 1 nM, inhibits cell growth in the H146 and H1417 small-cell lung cancer cell lines with IC{sub 50} values of 60-90 nM, and induces robust cell death in the H146 cancer cell line at 30-100 nM.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Introduction to and comparison of deep learning and optimization approaches to analytical wake modeling of a tilted wind turbine

This paper introduces innovative optimization and deep learning techniques to enhance the prediction of complex wake dynamics in the downstream wind velocity of tilted wind turbines. Traditional methods for calibrating the Bastankhah wake model often lead to increased errors in wind velocity distribution due to overfitting of the local wake characteristics. To address this issue, we propose an additional global optimization step to reduce errors in wind velocity predictions with respect to various wake parameters. Despite this improvement, the Bastankhah model's axisymmetric Gaussian wake shape limits its accuracy for complex wake structures. Therefore, we also propose a deep learning approach, which demonstrates promising results by accurately modeling complex wake shapes across a broader range of tilt angles with minimal computational cost. The deep learning approach achieves near-identical predictions to high-fidelity large-eddy simulations, representing a promising advancement in wake modeling.

17 WIND ENERGY↗
DOE OSTI2016

Combinatorial chemical bath deposition of CdS contacts for chalcogenide photovoltaics

Contact layers play an important role in thin film solar cells, but new material development and optimization of its thickness is usually a long and tedious process. A high-throughput experimental approach has been used to accelerate the rate of research in photovoltaic (PV) light absorbers and transparent conductive electrodes, however the combinatorial research on contact layers is less common. Here, we report on the chemical bath deposition (CBD) of CdS thin films by combinatorial dip coating technique and apply these contact layers to Cu(In,Ga)Se 2 (CIGSe) and Cu 2 ZnSnSe 4 (CZTSe) light absorbers in PV devices. Combinatorial thickness steps of CdS thin films were achieved by removal of the substrate from the chemical bath, at regular intervals of time, and in equal distance increments. The trends in the photoconversion efficiency and in the spectral response of the PV devices as a function of thickness of CdS contacts were explained with the help of optical and morphological characterization of the CdS thin films. The maximum PV efficiency achieved for the combinatorial dip-coating CBD was similar to that for the PV devices processed using conventional CBD. Finally, the results of this study lead to the conclusion that combinatorial dip-coating can be used to accelerate the optimization of PV device performance of CdS and other candidate contact layers for a wide range of emerging absorbers.

14 SOLAR ENERGY↗
DOE OSTI2022

THE DESIGN OF LAYER TIME OPTIMIZATION IN LARGE SCALE ADDITIVE MANUFACTURING WITH FIBER REINFORCED POLYMER COMPOSITES

In this study, we have developed a method to optimize a layer deposition time (a.k.a. layer time) for large-scale additive manufacturing (AM) via physics-based simulations. A long layer time leads to an over-cooled surface on which a new layer is deposited, and therefore, it may result in a weak bonding or debonding between layers, cracking, or warping. A short layer time leads to a high temperature of the structure due to insufficient cooling, and therefore, the structure may not be stiff enough and may collapse during manufacturing. Therefore, it is important to estimate the optimal layer time in additive manufacturing for a high-quality product. The temperature of a top layer right before deposition is recommended to be slightly higher than the glass temperature of the material. A temperature cooling was approximated to an exponential function of time, and the optimized layer time was obtained based on a target temperature while maintaining a minimal printing time. The material used is carbon fiber-reinforced polycarbonate (CF/PC), and the large-scale deposition system used is LSAM TM from Thermwood Corporation. Three different layer time cases were used for experiments, and a series of thermal images were obtained via an infra-red (IR) camera during the entire AM processes. AM process simulations were performed using a finite element method and the temperature profiles from the simulation were in good agreement with those from experiments. The layer time optimization was performed based on the temperature profiles from the simulations. A layer temperature with the optimal layer time was confirmed as the target temperature through simulation.

Jo, Eonyeon↗
DOE OSTI2017

Causal control design for wave energy converters with finite stroke

Control design is of central importance in the optimization of power generation from ocean wave energy converters (WECs). The problem is made challenging by the fact that the resource is vibratory and stochastic, with a low quality factor. It has recently been shown that when constrained to causality, the optimal feedback controller for a WEC is solvable in closed-form as a sign-indefinite LQG problem, for the case in which the WEC exhibits linear dynamics and quadratic power transmission dissipation. However, most WECs exhibit stroke limits, which must be explicitly accounted for in control design to avoid end-stop collisions. This is important because such collisions lead to damage, and also because the introduction of unmodeled stroke limits can actually destabilize the otherwise-optimal closed loop. We illustrate a control design technique to address this issue, which consists of two design steps. In the first step, LMI techniques are used to optimize the mean power harvested from waves with a known power spectral density, subject to several competing objectives. In the second step, the controller is augmented via a nonlinear passivity-based outer-loop design which guarantees to prevent stroke saturation while also guaranteeing to preserve closed-loop stability. Furthermore, the technique is illustrated using a model of a simple heaving point absorber, and verified in simulation.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Approaching hydro-equivalent ignition in laser direct-drive via target design optimization using novel statistical modeling

Laser direct-drive offers significant advantages in terms of target simplicity, improved energy coupling, and large fuel masses over indirect drive. However, performance degradations from hydrodynamic and laser-plasma instabilities seeded and driven by the direct illumination pose limitations on the parameter space available for achieving ignition. In this paper, new design improvements are identified to forge a path forward for a hydro-equivalent ignition demonstration. The first is related to a new formulation of the statistical model (SM) used to accurately predict target performance directly from input parameters such as laser pulse shape and target specifications. This new SM formulation provides direct guidance on target dimensions and laser beam-to-target radius to achieve the highest fusion yield on the OMEGA laser. The second improvement comes from cooling the deuterium–tritium (DT) ice layer below the triple point right before shot time leading to lower DT vapor densities and higher convergence. Guided by these design improvements, a Bayesian optimization algorithm was used to design an implosion that is predicted to closely approach a Lawson triple product that hydrodynamically scales to ignition if equivalent laser–target coupling is achieved at laser energies typical of the National Ignition Facility.

Deuterium↗
DOE OSTI2025

Xopt and Badger: a machine learning ecosystem for real-time accelerator control and optimization

Machine learning (ML)-based black-box optimization algorithms have demonstrated significant improvements in accelerator optimization speed, often by orders of magnitude. However, deploying these algorithms in real-time facility control remains challenging due to the specialized expertise and infrastructure required. To bridge this gap, we introduce the Xopt ecosystem, a versatile suite of tools designed to make advanced ML-based optimization accessible to the broader accelerator community. This ecosystem includes Xopt, a modular Python framework that facilitates the integration of ML-based optimization algorithms with arbitrary control problems, and Badger, a graphical user interface built on top of Xopt, which enables seamless deployment of ML algorithms in real-time control systems. The Xopt ecosystem has been successfully applied towards solving challenging real-time control problems at leading international accelerator facilities, including SLAC, LBNL, Argonne, Fermilab, BNL, DESY, and ESRF, demonstrating its effectiveness in real-world optimization tasks. In this presentation, we provide an overview of Xopt’s capabilities and illustrate its impact through case studies from SLAC accelerator facilities including LCLS, LCLS-II, and FACET-II.

Roussel, Ryan [SLAC]↗
DOE OSTI2014

High Shear Deformation to Produce High Strength and Energy Absorption in Mg Alloys

Magnesium alloys have the potential to reduce the mass of transportation systems however to fully realize the benefits it must be usable in more applications including those that require higher strength and ductility. It has been known that fine grain size in Mg alloys leads to high strength and ductility. However, the challenge is how to achieve this optimal microstructure in a cost effective way. This work has shown that by using optimized high shear deformation and second phase particles of Mg2Si and MgxZnZry the energy absorption of the extrusions can exceed that of AA6061. The extrusion process under development described in this presentation appears to be scalable and cost effective. In addition to process development a novel modeling approach to understand the roles of strain and state-of-strain on particle fracture and grain size control has been developed

Extrusion↗
DOE OSTI2016

Valence-band electronic structure evolution of graphene oxide upon thermal annealing for optoelectronics

We report valence band electronic structure evolution of graphene oxide (GO) upon its thermal reduction. Degree of oxygen functionalization was controlled by annealing temperatures, and an electronic structure evolution was monitored using real-time ultraviolet photoelectron spectroscopy. We observed a drastic increase in density of states around the Fermi level upon thermal annealing at ~600 °C. The result indicates that while there is an apparent band gap for GO prior to a thermal reduction, the gap closes after an annealing around that temperature. This trend of band gap closure was correlated with electrical, chemical, and structural properties to determine a set of GO material properties that is optimal for optoelectronics. The results revealed that annealing at a temperature of ~500 °C leads to the desired properties, demonstrated by a uniform and an order of magnitude enhanced photocurrent map of an individual GO sheet compared to as-synthesized counterpart.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Dielectrophoresis assisted rapid, selective and single cell detection of antibiotic resistant bacteria with G-FETs

Time-consuming, expensive and low sensitivity diagnostic methods used for monitoring bacterial infections lead to unnecessary or delays in prescription of the right antibiotic treatment. Determining an optimal clinical treatment requires rapid detection and identification of pathogenic bacteria and their sensitivity to specific antimicrobials. However, diagnostic devices that meet all of these criteria have proven elusive thus far. Graphene field effect transistors (G-FET) are a promising solution, since they are highly sensitive to chemical/biological modification, can have fast detection times and can be placed on different substrates. Here, by integrating specific peptide probes over G-FETs, we present a proof-of-concept study for species and strain specific label-free detection of clinical strains of pathogenic bacteria with high specificity and sensitivity. We found that pyrene-conjugated peptides immobilized on G-FETs were capable of detecting pathogenic Staphylococcus aureus at the single-cell level and discriminate against other gram-positive and gram-negative bacterial pathogens. A similar device was able to discriminate between antibiotic resistant and sensitive strains of Acinetobacter baumannii , suggesting that these devices can also be used for detecting antibiotic resistive pathogens. Furthermore, a new means of enhancing attachment, electric-field assisted binding, reduced the detection limit to 104 cells/ml and the detection time to below 5 minutes. Here, the combination of single step attachment, inexpensive production, rapid, selective and sensitive detection suggest G-FETs plus pyrene-conjugated peptides are a new platform for solving major challenges faced in point of care diagnostics to fight infectious diseases and antimicrobial resistance.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2014

Effects of a gradually increased load of fish waste silage in co-digestion with cow manure on methane production

Highlights: • New results from continuous anaerobic co-digestion of fish waste silage (FWS) and cow manure (CM). • Co-digestion of FWS and CM has a high biogas potential. • Optimal mixing ratio of FWS/CM is 13–16/87–84 volume%. • High input of FWS leads to accumulation of NH4+ and VFAs and process failure. - Abstract: This study examined the effects of an increased load of nitrogen-rich organic material on anaerobic digestion and methane production. Co-digestion of fish waste silage (FWS) and cow manure (CM) was studied in two parallel laboratory-scale (8 L effective volume) semi-continuous stirred tank reactors (designated R1 and R2). A reactor fed with CM only (R0) was used as control. The reactors were operated in the mesophilic range (37 °C) with a hydraulic retention time of 30 days, and the entire experiment lasted for 450 days. The rate of organic loading was raised by increasing the content of FWS in the feed stock. During the experiment, the amount (volume%) of FWS was increased stepwise in the following order: 3% – 6% – 13% – 16%, and 19%. Measurements of methane production, and analysis of volatile fatty acids, ammonium and pH in the effluents were carried out. The highest methane production from co-digestion of FWS and CM was 0.400 L CH4 gVS{sup −1}, obtained during the period with loading of 16% FWS in R2. Compared to anaerobic digestion of CM only, the methane production was increased by 100% at most, when FWS was added to the feed stock. The biogas processes failed in R1 and R2 during the periods, with loadings of 16% and 19% FWS, respectively. In both reactors, the biogas processes failed due to overloading and accumulation of ammonia and volatile fatty acids.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Efficient solution-processed small molecule: Cadmium selenide quantum dot bulk heterojunction solar cells

We report bulk heterojunction solar cells based on blends of solution-processed small molecule [7,7′-(4,4-bis(2-ethylhexyl)-4H-silolo[3,2-b:4,5-b′]dithiophene-2,6-diyl) bis(6-fluoro-4-(5′-hexyl-[2,2′-bithiophen]-5yl)benzo[c] [1,2,5] thiadiazole)] p-DTS(FBTTh{sub 2}){sub 2}: Cadmium Selenide (CdSe) (70:30, 60:40, 50:50, and 40:60) in the device configuration: Indium Tin Oxide /poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS)/p-DTS(FBTTh{sub 2}){sub 2}: CdSe/Ca/Al. The optimized ratio of p-DTS(FBTTh{sub 2}){sub 2}:CdSe::60:40 leads to a short circuit current density (J{sub sc}) = 5.45 mA/cm{sup 2}, open circuit voltage (V{sub oc}) = 0.727 V, and fill factor (FF) = 51%, and a power conversion efficiency = 2.02% at 100 mW/cm{sup 2} under AM1.5G illumination. The J{sub sc} and FF are sensitive to the ratio of p-DTS(FBTTh{sub 2}){sub 2}:CdSe, which is a crucial factor for the device performance.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Hole-transport material variation in fully vacuum deposited perovskite solar cells

This work addresses the effect of energy level alignment between the hole-transporting material and the active layer in vacuum deposited, planar-heterojunction CH{sub 3}NH{sub 3}PbI{sub x−3}Cl{sub x} perovskite solar cells. Through a series of hole-transport materials, with conductivity values set using controlled p-doping of the layer, we correlate their ionization potentials with the open-circuit voltage of the device. With ionization potentials beyond 5.3 eV, a substantial decrease in both current density and voltage is observed, which highlights the delicate energetic balance between driving force for hole-extraction and maximizing the photovoltage. In contrast, when an optimal ionization potential match is found, the open-circuit voltage can be maximized, leading to power conversion efficiencies of up to 10.9%. These values are obtained with hole-transport materials that differ from the commonly used Spiro-MeO-TAD and correspond to a 40% performance increase versus this reference.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Characterizing magnetized plasmas with dynamic mode decomposition

Accurate and efficient plasma models are essential to understand and control experimental devices. Existing magnetohydrodynamic or kinetic models are nonlinear and computationally intensive and can be difficult to interpret, while often only approximating the true dynamics. In this work, data-driven techniques recently developed in the field of fluid dynamics are leveraged to develop interpretable reduced-order models of plasmas that strike a balance between accuracy and efficiency. In particular, dynamic mode decomposition (DMD) is used to extract spatio-temporal magnetic coherent structures from the experimental and simulation datasets of the helicity injected torus with steady inductive (HIT-SI) experiment. Three-dimensional magnetic surface probes from the HIT-SI experiment are analyzed, along with companion simulations with synthetic internal magnetic probes. A number of leading variants of the DMD algorithm are compared, including the sparsity-promoting and optimized DMD. Optimized DMD results in the highest overall prediction accuracy, while sparsity-promoting DMD yields physically interpretable models that avoid overfitting. These DMD algorithms uncover several coherent magnetic modes that provide new physical insights into the inner plasma structure. These modes were subsequently used to discover a previously unobserved three-dimensional structure in the simulation, rotating at the second injector harmonic. Finally, using data from probes at experimentally accessible locations, DMD identifies a resistive kink mode, a ubiquitous instability seen in magnetized plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Role of deep levels and barrier height lowering in current-flow mechanism in 150 μ m thick epitaxial n-type 4H–SiC Schottky barrier radiation detectors

Schottky barrier detectors (SBDs) require larger surface areas than conventional electronics to increase the detection efficiency although such SBDs manifest large diode ideality factors due to inhomogeneous areal distribution of surface barrier height (SBH). Inhomogeneous SBH distributions lead to various current flow mechanisms in SBDs, which need to be identified to optimize detector performance. In this Letter, we identify the current flow mechanism in large area Schottky barrier diodes for radiation detection fabricated on 150 μm thick n-4H–SiC epitaxial layers. The analysis of temperature-dependent forward current–voltage (I–V–T) characteristics of SBDs revealed two linear regions in current–voltage curves up to 450 K, one corresponding to the current flow through a low barrier patch, while the other corresponds to that of average barrier distribution. Applying a SBH distribution model to the reverse I–V–T characteristics, an activation energy of 0.76 eV for the current flow over the Schottky barrier was calculated. Additionally, the activation energy did not directly correspond to any of the defect levels observed from the deep level transient spectroscopy (DLTS). Above 450 K, a Schottky type barrier lowering suggested a current flow through a low barrier patch of ≈ 0.8 eV. The absence of any SBH lowering below 450 K indicated that the current corresponded to a neutrally charged trap level at ≈ 0.6 eV below the conduction band edge, which was consistent with DLTS measurements revealing the presence of an electron trap level Z1/2 at 0.59 eV below the conduction band edge.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Improved loss functions for machine-learned atomic potentials

Machine learning (ML) has become an invaluable tool across a wide array of domains in science as researchers find new ways to leverage its predictive power. This is especially true in chemistry, where ML is used to fit chemical properties or desirable attributes to the local structure of molecules and materials. In the pursuit of greater accuracy, it is relatively simple to increase the size or complexity of such models, although this often requires simultaneously seeking larger datasets in order to both fit and interpret the larger number of parameters. However, it is equally important to assess the quality and relative importance of the data and how these factors impact the training process. We, therefore, investigate the impact of using different loss functions for training neural network potentials (NNPs), as the loss function defines the error and parameter gradients used to train the NNP. In particular, we test the mean-squared error and Huber loss functions and, using insight from these functions, derive a new loss function based on the Asinh function, which yields significant improvement in the accuracy and generality of NNPs. We show that by discounting/minimizing errors and anomalies in the optimization process, both the Huber and Asinh loss functions improve the training of NNPs, leading to a final potential with a greater effective dimensionality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗