Search NASA⌕ Search

SEARCH · Search NASA

Results for “RESEARCH PROBLEMS”

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 37 records · Page 2

Rheological dynamics and structural characteristics of supramolecular assemblies of β-cyclodextrin and sulfonic surfactants

Cyclodextrins are highly functional compounds with a hydrophobic cavity capable of forming supramolecular inclusion complexes with various classes of molecules including surfactants. The resultant rich nanostructures and their dynamics are an interesting research problem in the area of soft condensed matter and related applications. In this paper, we report novel dynamical supramolecular assemblies based on the complexation of β-cyclodextrin with 3 different sulfonic surfactants, which are sodium hexadecylsulfate, sodium dodecylbenzenesulfonate, and myristyl sulfobetaine. It was observed that a β-cyclodextrin : surfactant/2 : 1 molar ratio was ideal for inducing axial growth and imparting large viscosities in the suspensions. Such complexation processes were accompanied by intriguing nanostructural phase behaviors and rheological properties that were very sensitive to the molecular architecture of sulfonic surfactants. The presence of an amino group in the head group of the surfactant allowed for large viscosities that reached 2.4 × 10 4 Pa s which exhibited gel-like behavior. In contrast, smaller viscosity values with a lower consistency index were observed when a bulky aromatic ring was present instead. DIC microscopy was used to visually probe the microstructure of the systems with respect to sulfonate molecular architecture. Additionally, surface tension measurements, and FTIR and NMR spectroscopies were used to gain insights into the nature of interactions that lead to the complexation and nanostructural characteristics. Finally, mechanics correlating the supramolecular morphologies to the rheological properties were proposed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Metasurface-enhanced photochemical activity in visible light absorbing semiconductors

Heterogeneous photocatalysis is an important research problem relevant to a variety of sustainable energy technologies. However, obtaining high photocatalytic efficiency from visible light absorbing semiconductors is challenging due to a combination of weak absorption, transport losses, and low activity. Aspects of this problem have been addressed by multilayer approaches, which provide a general scheme for engineering surface reactivity and stability independent of electronic considerations. However, an analogous broad framework for optimizing light–matter interactions has not yet been demonstrated. Here, we establish a photonic approach using semiconductor metasurfaces that is highly effective in enhancing the photocatalytic activity of GaAs, a high-performance semiconductor with a near-infrared bandgap. Our engineered pillar arrays with heights of ∼150 nm exhibit Mie resonances near 700 nm that result in near-unity absorption and exhibit a field profile that maximizes charge carrier generation near the solid–liquid interface, enabling short transport distances. Our hybrid metasurface photoanodes facilitate oxygen evolution and exhibit enhanced incident photon-to-current efficiencies that are ∼22× larger than a corresponding thin film for resonant excitation and 3× larger for white light illumination. Key to these improvements is the preferential generation of photogenerated carriers near the semiconductor interface that results from the field enhancement profile of magnetic dipolar-type modes.

Chemistry↗

Training models using forces computed by stochastic electronic structure methods

Abstract Quantum Monte Carlo (QMC) can play a very important role in generating accurate data needed for constructing potential energy surfaces. We argue that QMC has advantages in terms of a smaller systematic bias and an ability to cover phase space more completely. The stochastic noise can ease the training of the machine learning model. We discuss how stochastic errors affect the generation of effective models by analyzing the errors within a linear least squares procedure, finding that there is an advantage to having many relatively imprecise data points for constructing models. We then analyze the effect of noise on a model of many-body silicon finding that noise in some situations improves the resulting model. We then study the effect of QMC noise on two machine learning models of dense hydrogen used in a recent study of its phase diagram. The noise enables us to estimate the errors in the model. We conclude with a discussion of future research problems.

Ceperley, David M. (ORCID:0000000150826271)↗

H-lignin can be deposited independently of CINNAMYL ALCOHOL DEHYDROGENASE C and D in Arabidopsis

Abstract Lignin contributes substantially to the recalcitrance of biomass toward saccharification. To circumvent this problem, researchers have genetically altered lignin, although, in a number of cases, these efforts have resulted in an undesirable yield penalty. Recent findings have shown that by knocking out two subunits (MED5A and MED5B) of the transcriptional regulatory complex Mediator, the stunted growth phenotype of mutants in p-coumaroyl shikimate 3′-hydroxylase, reduced epidermal fluorescence 8-1 (ref8-1), can be alleviated. Furthermore, these plants synthesize a lignin polymer almost entirely derived from p-coumaryl alcohol. Plants deficient in cinnamyl alcohol dehydrogenase (CAD) are notable in that they primarily incorporate coniferaldehyde and sinapaldehyde into their lignin. We tested the hypothesis that by stacking mutations in the genes encoding for the CAD paralogs C and D on an Arabidopsis (Arabidopsis thaliana) med5a/5b ref8-1 genetic background, the biosynthesis of p-coumaryl alcohol would be blocked, making p-coumaraldehyde available for polymerization into a novel kind of lignin. The med5a/5b ref8-1 cadc cadd plants are viable, but lignin analysis demonstrated that they continue to synthesize p-hydroxyphenyl lignin despite being mutated for the CADs typically considered to be required for monolignol biosynthesis. In addition, enzyme activity tests showed that even in the absence of CADC and CADD, there is high CAD activity in stems. We tested the potential involvement of other CADs in p-coumaraldehyde biosynthesis in the quintuple mutant by mutating them using the CRISPR/Cas9 system. Lignin analysis demonstrated that the resulting hextuple mutant plants continue to deposit p-coumaryl alcohol-derived lignin, demonstrating a route for the synthesis of p-hydroxyphenyl lignin in Arabidopsis independent of four CAD isoforms.

59 BASIC BIOLOGICAL SCIENCES↗

Quantifying Energy Conversion in Higher-Order Phase Space Density Moments in Plasmas

Weakly collisional and collisionless plasmas are typically far from local thermodynamic equilibrium (LTE), and understanding energy conversion in such systems is a forefront research problem. The standard approach is to investigate changes in internal (thermal) energy and density, but this omits energy conversion that changes any higher-order moments of the phase space density. In this Letter, we calculate from first principles the energy conversion associated with all higher moments of the phase space density for systems not in LTE. Particle-in-cell simulations of collisionless magnetic reconnection reveal that energy conversion associated with higher-order moments can be locally significant. Furthermore, the results may be useful in numerous plasma settings, such as reconnection, turbulence, shocks, and wave-particle interactions in heliospheric, planetary, and astrophysical plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Ecological Adaptive Cruise Control of Plug-In Hybrid Electric Vehicle With Connected Infrastructure and On-Road Experiments

Abstract This paper examines both mathematical formulation and practical implementation of an ecological adaptive cruise controller (ECO-ACC) with connected infrastructure. Human errors are typical sources of accidents in urban driving, which can be remedied by rigorous control theories. Designing an ECO-ACC is, therefore, a classical research problem to improve safety and energy efficiency. We add two main contributions to the literature. First, we propose a mathematical framework of an online ECO-ACC for plug-in hybrid electric vehicle (PHEV). Second, we demonstrate ECO-ACC in a real world, which includes other human drivers and uncertain traffic signals on a 2.6 (km) length of the corridor with eight signalized intersections in Southern California. The demonstration results show, on average, 30.98% of energy efficiency improvement and 8.51% additional travel time.

Automation & Control Systems↗

CPAP Ventilators Needed for Rapid Response to COVID-19 by Modification of CPAP Equipment

Early on in the COVID-19 pandemic, potential ventilator shortages were a critical issue identified by national health care providers. Capacity modeling at the time suggested patient demand may exceed ventilator supply. Thus, the challenge became finding an urgent interim solution to meet health care needs. Our initial hypothesis was that CPAP technology could be modified to provide similar functionality to a ventilator, relieving demand and allowing physicians to decide which patients need high end machines, ultimately saving lives. In conjunction with medical experts and pulmonologists, we were able to identify three key thrusts associated with this research problem: (1) modification of CPAP technology to allow for 02 input that would be capable of providing ventilation; (2) development of an alarming function that would provide real-time audible alarms to alert medical personnel to critical conditions, which would be used inline with CPAP technology; and (3) a method of sterilizing expiratory air from such a system in order to protect medical personnel from biohazard, since CPAPs vent to the atmosphere. We were unable to realize results for thrust 1 (CPAP modification for 02); we identified potential safety issues associated with utilizing medical grade oxygen with a common CPAP device. In order to characterize and mitigate these issues, we would need to partner closely with a device manufacturer; such a partnership could not be achieved in the timeframe needed for this rapid response work. However, we determined that some medical grade BiPAP devices do not need this modification and that the significant progress on thrusts 2 and 3 would be sufficient to buy down risk of a massive ventilator shortage. Our team built a prototype alarm system that can be utilized with any assistive respiratory device to alert on all key conditions identified by medical personnel (high pressure, low pressure, apnea, loss of power, low battery). Finally, our team made significant progress in the rapid prototyping and demonstration of an inline UV air purifier device. The device is cost efficient and can be manufactured at scale with both commercially available and additively manufactured parts. Initial tests with SARS-CoV-2 analog bacteriophage MS2 show 99% efficacy at reducing bioburden. Following a successful demonstration of the prototype device with medical personnel, we were able to obtain follow-on (non-LDRD) funding to provide additional device characterization, validation, and production in order to respond to an immediate regional need.

42 ENGINEERING↗

Fast response High Altitude Laser Optical Sensor (HALOS) for atmospheric water and temperature measurements

Advanced instrumentation and sensing methods are the key factor in the determination of many climate science research problems. In this Phase I STTR a laser-based diagnostic system for atmospheric temperature and water concentrations measurements was developed, this system, HALOS – High Altitude Laser Optical Sensor, employs a mid-infrared (MIR) quantum cascade laser (QCL) of the distributed feedback variety (DFB) powered by a 100 kHz rep rate ramp modulated current profile which enables spectral profiling. Measurements made during Phase I showed that HALOS can surpass the available hygrometers refresh rates while offering superior accuracy (<4%) from the low Frost Point (Tfrost) of -110°C to dew points (Tdew) at 35°C while being able to encompass sub ppm concentration at altitude and supersaturated conditions at ground level. Readings from HALOS include temperature sensitivity on the order of 0.2°C and absolute humidity sensitivity (AH) on the order of 0.010 μmol/m 3 (0.18 mg/m 3 ) at a data output rate exceeding 1 kHz. HALOS can be easily integrable in existing Department of Energy (DOE) or National Oceanic and Atmospheric Administration (NOAA) conventional or remote operated aircraft having either a fixed or rotary wing.

54 ENVIRONMENTAL SCIENCES↗

Cybersecurity Lessons Learned from Vehicle to Grid Engagement

As the transportation industry continues to become electrified, introduction of additional digital devices within associated actions such as recharging bring additional potential for cybersecurity attacks. Devices that are designed, implemented, and operated with cybersecurity as a crucial consideration exacerbate these concerns by failing to provide strict boundaries on access to and use of the equipment. Emerging use cases such as Vehicle to Grid (V2G) charging may expand the potential physical effects of a cybersecurity attack by providing indirect access to electrical components of a building microgrid or portions of the larger power grid. This paper serves as an overview of findings and recommendations based on cybersecurity testing performed at a V2G implementation site operated by a member of the Memorandum of Understanding (MOU) to Establish the Vehicle-to-Everything (V2X) Collaboration [1]. The Department of Energy Office of Cybersecurity, Energy Security, and Emergency Response is a signatory of the MOU, and has funded this research paper and associated body of work regarding V2X cybersecurity. Sandia has a large background of previous research focused on Electric Vehicle (EV) cybersecurity, such as reference [2], which includes an overall survey of EV infrastructure cybersecurity and recommendations based on those findings. This report seeks to expand knowledge of EV cybersecurity status and needs by focusing on a specific implementation of V2G charging, and providing recommendations based on the relevant findings. This report serves as a publicly available, sanitized description of applied vulnerability testing on an operational V2G implementation. A more in-depth technical version of the report is provided to the MOU partner, but not available at the time of writing due to inclusion of proprietary information. V2G charging comes with many research problems that must be solved before the technology can securely implemented in sites with unrestricted public access or where cybersecurity attacks could have increased consequences, such as government offices. V2G charging requires many stakeholders such as end users, host sites, equipment vendors, and integrators, which all rely on operational safety and security as well as security and trustworthiness of any associated financial transactions.

33 ADVANCED PROPULSION SYSTEMS↗

The First Tri-Lab Workshop on Formal Verification: Capabilities, Challenges, Research Opportunities, and Exemplars

The First Tri-Lab Workshop on Formal Verification was held in Santa Fe, New Mexico, on December 5th, 2023. This workshop gathered staff from Sandia, Los Alamos, and Lawrence Livermore National Laboratories and NASA’s Jet Propulsion Laboratory. This report summarizes and expands on the presentations given and discussion had at this workshop. In this report, we describe the current capabilities and research needs related to formal methods at the NNSA labs. In particular, we identify medium-term and long-term research gaps in programming languages, formalization efforts of complex systems, embedded systems verification, hardware verification, cybersecurity, formal methods usability, workflows, numerical methods, the use of formal methods for artificial intelligence (and its converse, artificial intelligence for formal methods), and collaboration opportunities and considerations on these topics. We conclude with a small number of exemplar research problems related to these topics.

97 MATHEMATICS AND COMPUTING↗

Agile collaboration: Citizen science as a transdisciplinary approach to heliophysics

Citizen science connects scientists with the public to enable discovery, engaging broad audiences across the world. There are many attributes that make citizen science an asset to the field of heliophysics, including agile collaboration. Agility is the extent to which a person, group of people, technology, or project can work efficiently, pivot, and adapt to adversity. Citizen scientists are agile; they are adaptable and responsive. Citizen science projects and their underlying technology platforms are also agile in the software development sense, by utilizing beta testing and short timeframes to pivot in response to community needs. As they capture scientifically valuable data, citizen scientists can bring expertise from other fields to scientific teams. The impact of citizen science projects and communities means citizen scientists are a bridge between scientists and the public, facilitating the exchange of information. These attributes of citizen scientists form the framework of agile collaboration. In this paper, we contextualize agile collaboration primarily for aurora chasers, a group of citizen scientists actively engaged in projects and independent data gathering. Nevertheless, these insights scale across other domains and projects. Citizen science is an emerging yet proven way of enhancing the current research landscape. To tackle the next-generation’s biggest research problems, agile collaboration with citizen scientists will become necessary.

79 ASTRONOMY AND ASTROPHYSICS↗

Utilization of Additive Manufacturing for the Rapid Prototyping of C-Band Radiofrequency Loads

Additive manufacturing is a versatile technique that shows promise in providing quick and dynamic manufacturing for complex engineering problems. Research has been ongoing into the use of additive manufacturing for potential applications in radiofrequency (RF) component technologies. Here, we present a method for developing an effective prototype load produced from 316L stainless steel on a direct metal laser sintering machine. The model was tested using simulation software to verify the validity of the design. The load structure was manufactured by an online digital manufacturing company, showing the viability of using easily accessible tools to manufacture RF structures. The produced load was able to produce an S11 value of −22.8 dB at a C-band frequency of 5.712 GHz while under a vacuum. In a high-power test, the load was able to terminate a peak power of 8.1 MW. The discussion includes future applications of the present method and how it will help to improve the implementation of future accelerator concepts.

36 MATERIALS SCIENCE↗

Improving Land Cover Classification Using Extended Multi-Attribute Profiles (EMAP) Enhanced Color, Near Infrared, and LiDAR Data

Hyperspectral (HS) data have found a wide range of applications in recent years. Researchers observed that more spectral information helps land cover classification performance in many cases. However, in some practical applications, HS data may not be available, due to cost, data storage, or bandwidth issues. Instead, users may only have RGB and near infrared (NIR) bands available for land cover classification. Sometimes, light detection and ranging (LiDAR) data may also be available to assist land cover classification. A natural research problem is to investigate how well land cover classification can be achieved under the aforementioned data constraints. In this paper, we investigate the performance of land cover classification while only using four bands (RGB+NIR) or five bands (RGB+NIR+LiDAR). A number of algorithms have been applied to a well-known dataset (2013 IEEE Geoscience and Remote Sensing Society Data Fusion Contest). One key observation is that some algorithms can achieve better land cover classification performance by using only four bands as compared to that of using all 144 bands in the original hyperspectral data with the help of synthetic bands generated by Extended Multi-attribute Profiles (EMAP). Moreover, LiDAR data do improve the land cover classification performance even further.

58 GEOSCIENCES↗

Discriminative Dimensionality Reduction using Deep Neural Networks for Clustering of LIGO Data

In this paper, leveraging the capabilities of neural networks for modeling the non-linearities that exist in the data, we propose several models that can project data into a low dimensional, discriminative, and smooth manifold. The proposed models can transfer knowledge from the domain of known classes to a new domain where the classes are unknown. A clustering algorithm is further applied in the new domain to find potentially new classes from the pool of unlabeled data. The research problem and data for this paper originated from the Gravity Spy project which is a side project of Advanced Laser Interferometer Gravitational-wave Observatory (LIGO). The LIGO project aims at detecting cosmic gravitational waves using huge detectors. However non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of LIGO. This is undesirable as it creates problems for the gravitational wave detection process. Gravity Spy aids in glitch identification with the purpose of understanding their origin. Since new types of glitches appear over time, one of the objective of Gravity Spy is to create new glitch classes. Towards this task, we offer a methodology in this paper to accomplish this.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Data and Tools for Energy Planning and Analysis

The National Renewable Energy Laboratory (NREL) creates widely used data and tools to facilitate energy system planning and analysis. These software tools have been developed for complex research problems and perfected over real-world applications and laboratory validations. Some tools are award winners, others are open-source data explorers, and all are rigorously designed to empower decision-makers with accurate and accessible information. This software selection shows how NREL resources can help stakeholders achieve a clean, just, and resilient energy transformation.

data↗

Pre-trained network-based transfer learning: A small-sample machine learning approach to nuclear power plant classification problem

Some research topics belonging to classification problems in the nuclear industry, such as fault diagnosis and accident identification, can be solved by feature extraction and subsequent application of statistical machine learning classifiers. Recently, deep neural network-based methods with automatic feature extraction and high accuracy have gained wide attention. They usually require large-scale training data, however, plant fault or accident data are scarce or difficult to obtain. Here this paper proposes a convolutional network (CNN)-based transfer learning method to solve this problem. The network's shallow layer is derived from a pre-trained CNN based on the ImageNet database to automatically extract features, and the deep layer is customized to match the classification problem. Data in non-image formats are converted to image formats and subsequently used to train the network. Case studies of rotating machines fault diagnosis show that the proposed method requires only limited training data to achieve high accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

A framework for multirate time integration of interface-coupled problems

The research described here was performed as part of the DOE SciDAC project Coupling Approaches for Next Generation Architectures (CANGA). A framework was developed for the derivation of novel algorithms for the multirate time integration of two-component systems coupled across an interface between spatial domains. The multirate aspect means that different time steps are allowed by each component integrator. The framework provides a way to construct multirate integrators with desirable properties related to stability, accuracy and preservation of system invariants. This report describes the framework and summarizes the major results, examples and research products.

97 MATHEMATICS AND COMPUTING↗

Delivering Contextual Knowledge and Critical Skills of Disruptive Technologies through Problem-Based Learning in Research Experiences for Undergraduates Setting

The recent development in transportation, such as energy-efficient and autonomous vehicles, defines a condition for the students in transportation engineering. Students in the field of transportation engineering should be ready upon their graduation with new knowledge and skills that are compatible with the need of the industry and sustainable engineering practices.During summers of 2018 and 2019, we developed and implemented an eight-week program to increase the knowledge and skills of students coming from multidisciplinary fields related to autonomous vehicles. Problem of “How much will platooning reduce fuel consumption and emissions per vehicle mile traveled?” was instrumentalized in subsequent activities to introduce the comprehensive knowledge structure of autonomous vehicles.The engineering concept of reducing the cost and sustainability was embedded in the leading research question that helped us to develop and implement activities on an overall knowledge structure in autonomous vehicles. The goal of using problem-based learning activities was not to encourage the students to focus on reaching the solution merely. We aimed to introduce the multidisciplinary knowledge and critical skills aspects of learning about disruptive technologies.In this paper, we will discuss how a multidisciplinary research approach was incorporated into a problem-based learning activity. The students were introduced the subjects related to math, physics, computer science, and biology as the integration of the knowledge structure of autonomous vehicles. We will also present the results on students’ use of critical skills such as machine learning and computer programming.

Comert, Gurcan↗