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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Development of an Optimized Gasoline Surrogate Formulation for PACE Experiments and Simulations

New powertrain solutions are needed to address societal challenges stemming in part from a transportation sector which relies heavily on combustion of conventional hydrocarbon fuels like gasoline. One way to advance new solutions is through predictive simulations. Predictive simulations can potentially limit the extent of experimental validations, reduce time spent in engineering design cycles, unlock new strategies for high efficiency combustion with power density, and minimize tailpipe emissions. However, the simulation tools currently available are either too computationally expensive, inadequate in their accuracy, or a combination of the two. A DOE-funded consortium of national laboratories called the Partnership to Advance Combustion Engines (PACE) seeks to address this gap by rapidly delivering new knowledge and tools which enable market-competitive powertrain solutions for light-duty vehicles. Approaches to modeling combustion in powertrain systems typically incorporate computational fluid dynamics (CFD) simulations with chemical kinetic models. These CFD simulations generally use surrogate fuels featuring a limited number of existing components in a reduced kinetic model to limit computational costs. Such surrogate fuels often sacrifice matching several or more combustion and physical properties of the target gasoline fuel. Validation of surrogate fuels is also often only pursued for a small subset of standard metrics such as research or motor octane number (RON, MON). The purpose of this project is to develop an optimal E10 gasoline surrogate fuel suitable for tasks across PACE to facilitate rapid common analysis and progression toward consortium goals. This project received contributions from tasks led by eight PACE principal investigators. The project funding here reflects all tasks under Pitz, whose tasks also contributed to additional PACE projects. Based on the optimal gasoline surrogate fuel composition, a reduced chemical kinetic model will be shared with PACE researchers, industry, and the broader combustion community.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Design and pre-flight performance of SPIDER 280 GHz receivers

In this work we describe upgrades to the Spider balloon-borne telescope in preparation for its second flight, currently planned for December 2021. The Spider instrument is optimized to search for a primordial B-mode polarization signature in the cosmic microwave background at degree angular scales. During its first flight in 2015, Spider mapped ~10% of the sky at 95 and 150 GHz. The payload for the second Antarctic flight will incorporate three new 280 GHz receivers alongside three refurbished 95- and 150 GHz receivers from Spider's first flight. In this work we discuss the design and characterization of these new receivers, which employ over 1500 feedhorn-coupled transition-edge sensors. We describe pre-flight laboratory measurements of detector properties, and the optical performance of completed receivers. These receivers will map a wide area of the sky at 280 GHz, providing new information on polarized Galactic dust emission that will help to separate it from the cosmological signal.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Meiotic susceptibility for induction of sperm with chromosomal aberrations in patients receiving combination chemotherapy for Hodgkin lymphoma

Improvements in survival rates with gonad-sparing protocols for childhood and adolescence cancer have increased the optimism of survivors to become parents after treatment. Findings in rodents indicate that chromosomal aberrations can be induced in male germ cells by genotoxic exposures and transmitted to offspring and future generations with effects on development, fertility and health. Thus, there is a need for effective technologies to identify human sperm carrying chromosomal aberrations to assess the germ-line risks, especially for cancer survivors who have received genotoxic therapies. The time-dependent changes in the burden of sperm carrying structural chromosomal aberrations were assessed for the first time in a cancer setting, using the AM8 sperm FISH protocol which simultaneously detects abnormalities in chromosomal structure and number in sperm. Nine Hodgkin lymphoma (HL) patients provided 20 semen samples before, during, and after NOVP therapy (Novantrone, Oncovin, Velban and Prednisone) and radiation therapy that produced scattered gonadal doses from <0.05 to 0.6 Gy. Late meiosis was found to be the most sensitive to NOVP treatment for the production of sperm with chromosomal abnormalities, both in structure and number. Earlier stages of spermatogenesis were less sensitive and there was no evidence that therapy-exposed stem cells resulted in increased frequencies of sperm with abnormalities in chromosomal structure or number. This indicates that NOVP therapy may increase the risks for paternal transmission of chromosomal structural aberrations for sperm produced 32 to 45 days after a treatment with these drugs and implies that there are no excess risks for pregnancies conceived more than 6 months after this therapy. This clinical evaluation of the AM8 sperm FISH protocol indicates that it is a promising tool for assessing an individual’s burden of sperm carrying chromosomal structural aberrations as well as aneuploidies after cancer therapy, with broad applications in other clinical and environmental situations that may pose aneugenic or clastogenic risks to human spermatogenesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Second Target Station Project: LANSCE WNR Target 2 (Blue Room) Experiment 2022

The Second Target Station (STS) at SNS will address emerging scientific challenges by providing a source of intense, cold neutrons to instruments optimized for this source, using rotating, tungsten target blocks. The STS target will receive 1.3 GeV proton beam pulses from the SNS accelerator at a repetition rate of 15 Hz. The facility life is planned for 40 years, and each target assembly life is expected to be approximately 10 years. An accurate strain prediction is then critical for fatigue life assessment of STS target blocks because they will be subject to approximately 108 beam pulses per lifetime. As an R&D activity, the LANSCE WNR Target 2 (blue room) facility was used to test the strain response of prototypical target blocks to the thermal shock of a proton pulse. The blue room was well suited for a pulsed proton beam impact test of subscale STS target blocks; the 800 MeV proton energy is approximately 60% of the 1.3 GeV proton energy expected from the SNS accelerator to the STS. Both the LANSCE Proton Storage Ring (PSR) and SNS are short-pulse proton beam sources with nominal pulse widths of 250 ns and 661 ns, respectively, so the energy deposition in the target occurs in < 1 microsecond pulse duration.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

HALOS (Heliostat Aimpoint and Layout Optimization Software) [SWR-21-41]

Heliostat Aimpoint and Layout Optimization Software (HALOS) is an open-source software package that allows users to explore solar field layout optimization, aimpoint strategy optimization, and performance characterization of concentrating solar power tower plants. Users interface with the tool through python, and results are reported in time series tables, plots, runtime logs, and flat-file outputs. Users choose from a list of variables such as tower height, receiver capacity, flux limits, design-point irradiance, etc., and specify information about the system using a small collection of flat files. The software can then optimize the specified variables (e.g., aimpoints for each heliostat) to maximize the thermal energy delivered to the receiver while adhering to flux limits. HALOS is implemented to be flexible with respect to flux characterization methods, but includes a direct connection to NREL's SolarPILOT™ software via its python API so that users can utilize high-fidelity flux simulation methods that have already been developed.

Zolan, Alexander↗
DOE OSTI2021

Systems and methods for fast detection of elephant flows in network traffic

In a system for efficiently detecting large/elephant flows in a network, the rate at which the received packets are sampled is adjusted according to a top flow detection likelihood computed for a cache of flows identified in the arriving network traffic. After observing packets sampled from the network, Dirichlet-Categorical inference is employed to calculate a posterior distribution that captures uncertainty about the sizes of each flow, yielding a top flow detection likelihood. The posterior distribution is used to find the most likely subset of elephant flows. The technique rapidly converges to the optimal sampling rate at a speed O(1/n), where n is the number of packet samples received, and the only hyperparameter required is the targeted detection likelihood.

Gudibanda, Aditya↗
DOE OSTI2024

Optimized Incorporation of Furan into Diketopyrrolopyrrole-Based Conjugated Polymers for Organic Field-Effect Transistors

Flexible electronics have received considerable attention in the past decades due to their promising application in rollable display screens, wearable devices, implantable devices, and other electronic applications. Here, in particular, conjugated polymers are favored for flexible electronics due to their mechanical flexibility and potential for solution-processed fabrication techniques, such as blade-coating, roll-to-roll printing, and high-throughput printing allowing for high-performance transistor devices. Thiophene is the prevailing conjugated unit to construct these conjugated polymers due to its favorable electronic properties. On the other hand, furans are among the few conjugated moieties that are easily derived from bio renewable resources. To promote sustainability, we selectively introduced furan into the conjugated backbone of a high-mobility polymer scaffold and systematically studied the effect on the microstructure and charge transport. We show that partially and selectively replacing thiophene units with furan can yield nearly comparable performance compared to the all-thiophene polymer. This strategy offers an improvement in the sustainability of the polymer by incorporating bio-sourced furan without sacrificing the high-performance characteristics. Meanwhile, polymers with incorrect or complete furan incorporation show reduced mobilities. This work serves to develop coherent structure–morphology–performance relationships; such knowledge will establish guidelines for the future development of sustainable, furan-based conjugated materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Advancing Concentrating Solar Thermal Modeling Using System Advisor Model (SAM)

Concentrating solar thermal (CST) technologies play a critical role in enabling dispatchable power and high-temperature industrial heat applications. Accurate and flexible modeling tools are essential for evaluating system performance, guiding technology research and development, and informing investment decisions. The National Laboratory of the Rockies's System Advisor Model (SAM) is a widely used techno-economic simulation platform for CST systems, providing detailed performance and financial modeling capabilities for multiple CST system configurations. SAM integrates physics-based performance models with financial analysis to simulate the behavior of complex energy systems under realistic operating conditions. For CST technologies (including tower, parabolic trough, and linear Fresnel), SAM enables hourly simulations using site-specific weather data that ensure feasible operating conditions and convergence of mass and energy between core system components (i.e., solar field, receiver, thermal energy storage, and power cycle). These capabilities allow researchers and developers to evaluate annual energy production, capacity factors, levelized cost of energy (LCOE), and system dispatch strategies. A key advantage of SAM lies in its flexibility for parametric analysis and large-scale computational studies. Users can vary system design parameters such as heliostat field layout, receiver dimensions, thermal energy storage capacity, power block sizing, and installation cost assumptions to investigate their impact on system performance and financial metrics. When combined with automated scripting through LK, SDKTool, or Python interfaces, SAM enables high-throughput simulation workflows that support sensitivity analysis, technology benchmarking, and optimization studies. These approaches are particularly valuable for next-generation CST concepts, where design spaces are large and system interactions are complex. Another important capability of SAM is its support for dispatch optimization and thermal energy storage modeling, which are central to the value proposition of CST technologies. The ability to simulate integrated storage and flexible power generation allows researchers to explore strategies that maximize grid value, improve capacity utilization, and enhance integration with variable resources such as photovoltaic and wind generation. This poster will present an overview of SAM's thermal system modeling capabilities including concentrating solar. Additionally, we will highlight new feature developments including: 1) implementing Google's OR-Tools optimization platform for faster and more robust dispatch optimization, 2) developing a new power load following controller for modeling behind-the-meter applications, 3) enabling direct modeling of CSP-PV hybrid systems with the inclusion of battery storage, and 4) developing a multi-receiver falling particle Gen3 system model.

14 SOLAR ENERGY↗
DOE OSTI2025

Simulation and measurement of the dynamic strain response in a prototypical spallation target

The Second Target Station (STS) at the Spallation Neutron Source (SNS) will address emerging scientific challenges by providing a source of intense cold neutrons to instruments optimized for this source. The STS target will use rotating tungsten blocks and will receive 1.3 GeV proton beam pulses from the SNS accelerator at a repetition rate of 15 Hz. The facility life is planned for 40 years, and each target assembly life is expected to be approximately 10 years. An accurate strain prediction is then critical for fatigue life assessment of STS target blocks because they will be subject to approximately 10 8 beam pulses per lifetime. As an R&D activity, the Los Alamos Neutron Science Center (LANSCE) Weapons Neutron Research (WNR) Target 2 (Blue Room) facility was used to test the strain response of prototypical target blocks to the thermal shock of a proton pulse. The blue room was well suited for a pulsed proton beam impact test of subscale STS target blocks; the 800 MeV proton energy is approximately 60 % of the 1.3 GeV proton energy expected from the SNS accelerator to the STS. The LANSCE Proton Storage Ring (PSR) and SNS are both short-pulse proton beam sources with nominal pulse widths of 250 ns and 661 ns, respectively, so the energy deposition in the target occurs in <1 μs pulse duration. Strain measurements on the outer surface of three target blocks (bare tungsten, tantalum-clad tungsten, niobium-clad tungsten) were recorded for comparison against neutronics and structural simulations. In conclusion, this experiment and the supporting simulations satisfied the following primary research goals for the STS target.

Dynamic strain response↗
DOE OSTI2022

Differentiable Channels Are All You Need

Mobile wireless communication systems must adapt to and compensate for time-varying propagation paths. The process of performing compensation in the receiver for said effects is known as equalization. We seek an improved method for optimizing the reliability of said wireless links to minimize outage probability. A method based on machine learning, specifically deep learning, has the potential to allow for greater flexibility and robustness against unforeseen channel conditions. In this report we investigate the performance of a prior method in the literature with minor modifications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Secure eco-routing with databases under homomorphic encryption

A method for generating energy-optimized travel routes for a motor vehicle includes one or more of the following: receiving an origin destination (OD) of the motor vehicle and an encrypted energy consumption database of the motor vehicle; generating N candidate routes for the OD; evaluating encrypted energy consumption over a route using an encrypted energy consumption database; applying at least one of homomorphic addition function or homomorphic multiplication function to the encrypted energy consumption data; and returning N candidate routes and their encrypted energy consumption to a client.

Hegde, Bharatkumar↗
DOE OSTI2023

Cost-optimized Automated Variance Reduction for Highly Angle-dependent Radiation Transport Analyses [Dissertation]

Monte Carlo variance-reduction techniques that directly bias particle direction have historically received limited attention despite being useful for many radiation transport applications such as those in which radiation travels through large regions with a low probability of colliding. One such technique is known as DXTRAN (short for deterministic transport) in the MCNP Monte Carlo radiation transport code. Until now, effectively applying DXTRAN in calculations is largely based on empirical observations of computational performance. Optimal DXTRAN parameters are identified through manual iteration. This work develops new mathematical descriptions of the DXTRAN variance-reduction process and demonstrates a new automated variance-reduction method that applies these mathematical formulations to determine the optimal application of DXTRAN in a given problem.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Machine Learning Enabled Position Detection for 6.78 MHz UAV Wireless Power Transfer System

This paper presents a novel supervised machine learning (SML) approach for accurate position detection of the receiver coil in wireless power transfer (WPT) systems using only secondary-side electrical measurements, with applications in autonomous unmanned aerial vehicle (UAV) charging. The proposed method trains a supervised learning model to map measured secondary-side voltage and current features to the receiver’s spatial position with high precision. This enables an autonomous UAV to determine its location relative to the primary coil center, the optimal position for maximizing wireless charging efficiency. The sensing method is fully integrated into a standard WPT system, utilizing the same primary and secondary coils for both power transfer and position detection, thereby eliminating additional sensing hardware. The use of a 6.78 MHz operating frequency enhances positional sensitivity, as high-frequency near-field electromagnetic fields respond strongly to small spatial variations. Experimental validation is performed on a 30 W scaled prototype featuring a 210 mm × 140 mm primary coil, a 50 mm × 80 mm receiver coil, and a 15 mm air gap. Results demonstrate reliable position estimation and a strong correlation between predicted position and optimal coil alignment. This integrated framework unifying position detection and wireless charging offers a promising foundation for future autonomous electric vertical takeoff and landing (eVTOL) systems, enabling compact, hardware-efficient, and high-accuracy charging solutions.

Colak, Kerim [New York University]↗
DOE OSTI2021

Alignment frustration in block copolymer films with block copolymer grafted TiO 2 nanoparticles under soft-shear cold zone annealing

Block copolymers (BCPs) have received significant attention as promising candidates for sequestering nanoparticles and fabrication of aligned nanostructures with optimal optical or electrical properties. We investigate the influence of static and dynamic thermal field on the alignment of polystyrene-block-poly(methyl methacrylate) (PS-b-PMMA) BCP morphology with the loading of novel poly(methyl methacrylate-block-Polystyrene) (PMMA-b-PS)-grafted-TiO 2 nanoparticles (BCP-g-TiO 2 ). Observation of characteristics IR peaks for PMMA and PS in BCP-g-TiO 2 nanoparticles and Transmission Electron Microscopy (TEM) results of the outer coating of core nanoparticle, validate the grafting to approach in synthesizing BCP-g-TiO 2 . In this work, we report that under the sharp dynamic thermal field, at low loading of BCP-g-TiO 2 , there is good dispersion of nanoparticles in unidirectionally aligned BCP matrix in film interior probed by GISAXS, while, at high nanoparticle loading (~10 wt%), there is local frustration in the unidirectional alignment of the BCP matrix due to aggregation of BCP-g-TiO 2 nanoparticles. However, Grazing incidence small angle X-ray scattering (GISAXS) shows clearly that the BCP films remain largely locally ordered at the domain scale, despite these large perturbations to long-range ordering even at high loading level, while bringing in new TiO 2 functionality to the BCP films, such as UVO absorptivity or biofouling prevention, important to potential new applications of such membranes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Nowcasting Earthquakes: Imaging the Earthquake Cycle in California With Machine Learning

We propose a new machine learning-based method for nowcasting earthquakes to image the time-dependent earthquake cycle. The result is a timeseries that may correspond to the process of stress accumulation and release. The timeseries are constructed by using principal component analysis of regional seismicity. The patterns are found as eigenvectors of the cross-correlation matrix of a collection of seismicity timeseries in a coarse grained regional spatial grid (pattern recognition via unsupervised machine learning). The eigenvalues of this matrix represent the relative importance of the various eigenpatterns. Using the eigenvectors and eigenvalues, we compute the weighted correlation timeseries of the regional seismicity. This timeseries has the property that the weighted correlation generally decreases prior to major earthquakes in the region, and increases suddenly just after a major earthquake occurs. As in a previous paper, we find that this method produces a nowcasting timeseries that resembles the hypothesized regional stress accumulation and release process characterizing the earthquake cycle. We then address the problem of whether the timeseries contain information regarding future large earthquakes. For this, we compute a receiver operating characteristic and determine the decision thresholds for several future time periods of interest (optimization via supervised machine learning). We find that signals can be detected that can be used to characterize the information content of the timeseries. These signals may be useful in assessing present and near-future seismic hazards.

58 GEOSCIENCES↗
DOE OSTI2024

AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators

Tensor algebra operations represent an important class of algorithms used across many applications, including machine learning, scientific computing, and data analytics. As a result, the efficient generation of custom accelerators for tensor operations has received increased attention. Previous efforts have produced automated tools enabling users to prototype and explore optimized accelerators. However, little effort has been focused on the host-accelerator interaction in these tools. Efficient use of hardware accelerators requires knowledge about the accelerator's capabilities (operations, data formats, and opcode support), the host CPU microarchitecture (e.g., memory hierarchy), the host-accelerator interface, and the application's features (which code regions should be mapped onto an accelerator). Manually rewriting the original applications to facilitate improved custom accelerator mapping is an error-prone and time-consuming endeavor. To cope with this, we propose AXI4MLIR, a new framework to automatically generate and optimize the communication between the host CPU and arbitrary accelerators that implement linear algebra algorithms. AXI4MLIR extends the MLIR compiler framework to automatically generate efficient host-accelerator driver code for accelerators with AXI-based interfaces. Our compiler extensions enable automatic driver code generation while carefully considering the host's memory hierarchy and target accelerator features. To demonstrate the flexibility and utility of AXI4MLIR, we test it with diverse use cases that include different types of accelerators, tiling scenarios, and dataflow schemes. We compare our experimental results to manual implementations of host-accelerator driver code and find that our approach can reduce CPU cache references by 56% and deliver up to a 1.65x speedup.

Bohm Agostini, Nicolas↗
DOE OSTI2020

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one’s peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the “pattern” by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game decides among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make a similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information.

97 MATHEMATICS AND COMPUTING↗