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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enforcing Self-Consistent Kinematic Constraints in Neutrino Energy Estimators

Machine learning algorithms have long been utilized across many experimental collaborations within the neutrino physics community in applications to ascertain the singular kinematic quantity of initial neutrino energy for use in neutrino oscillation analyses. However, most of these algorithms do not incorporate a coherent physical picture of initial neutrino kinematics, opting to introduce loss functions involving knowledge of only |pν |. Here, we argue for the introduction of composite loss functions utilizing the full kinematic description of the neutrino, pν ≡ (E, px, py , pz ), compiling all relevant energy and angle information consistently. The use of such a fully defined variable can be seen as a usage of Physics Informed Machine Learning.

Richi, R. R.↗
NASA NTRS2007

Object Oriented Modeling and Design

The Object Oriented Modeling and Design seminar is intended for software professionals and students, it covers the concepts and a language-independent graphical notation that can be used to analyze problem requirements, and design a solution to the problem. The seminar discusses the three kinds of object-oriented models class, state, and interaction. The class model represents the static structure of a system, the state model describes the aspects of a system that change over time as well as control behavior and the interaction model describes how objects collaborate to achieve overall results. Existing knowledge of object oriented programming may benefit the learning of modeling and good design. Specific expectations are: Create a class model, Read, recognize, and describe a class model, Describe association and link, Show abstract classes used with multiple inheritance, Explain metadata, reification and constraints, Group classes into a package, Read, recognize, and describe a state model, Explain states and transitions, Read, recognize, and describe interaction model, Explain Use cases and use case relationships, Show concurrency in activity diagram, Object interactions in sequence diagram.

Shaykhian, Gholam Ali↗
NASA NTRSDate not supplied

Analyzing Launch Operations using the Spaceport Throughput Analysis Resource (STAR)

NASA’s Kennedy Space Center (KSC) and Cape CanaveralSpace Force Station (CCSFS) are the world’s preeminent multi-user spaceport, providing facilities and launch capabilities to the agency, NASA’s commercial partners, and other government agencies. KSC works to ensure an environment in which NASA’s programs and other users can safely and effectively carry out their operations. Approximately 100 launches are expected to take place in 2024. KSC needs to meet the needs of customers who request launches based on their own schedules, but who may not be aware of KSC-wide resource limitations and external constraints on launch operations. KSC-wide resources include telecommunications, range tracking, commodities such as Helium and Nitrogen, and special equipment needed to support launches. External constraints include seasonal launch operations. Since space vehicles are complex and launch operations are uncertain, unexpected events can also cause delays in operations. The Spaceport Throughput AnalysisResource (STAR) assesses whether a specific set of pro-posed launches and associated activities can be performed given the resources KSC currently has available, and external constraints imposed on KSC operations. STAR integrates short-horizon scheduling and simulation of launches using a monte-carlo approach driven by configurable probabilities of different classes of event outcomes, including de-lays and worst-case use of resources. The resulting analyses inform stakeholders of key constraints preventing customers from being able to perform their missions as desired, and give insight into how to improve spaceport throughput. The rest of the paper is organized as follows. In 2 we de-scribe the problem STAR is intended to solve. In Section3 we formally describe the scheduling problem ingredients. In Section 4 we describe how manifests are simulated. InSection 5 we describe the constraints problem that is solved when rescheduling. In Section 6 we describe what information is recorded during manifest simulation in order to evaluate delays. In Section 7 we describe the specific problem solved for KSC. In Section 8 we describe the challenges of knowledge engineering for STAR.

Planning Scheduling Analysis↗
NASA NTRS2007

Constraint, Intelligence, and Control Hierarchy in Virtual Environments

This paper seeks to deal directly with the question of what makes virtual actors and objects that are experienced in virtual environments seem real. (The term virtual reality, while more common in public usage, is an oxymoron; therefore virtual environment is the preferred term in this paper). Reality is difficult topic, treated for centuries in those sub-fields of philosophy called ontology- "of or relating to being or existence" and epistemology- "the study of the method and grounds of knowledge, especially with reference to its limits and validity" (both from Webster s, 1965). Advances in recent decades in the technologies of computers, sensors and graphics software have permitted human users to feel present or experience immersion in computer-generated virtual environments. This has motivated a keen interest in probing this phenomenon of presence and immersion not only philosophically but also psychologically and physiologically in terms of the parameters of the senses and sensory stimulation that correlate with the experience (Ellis, 1991). The pages of the journal Presence: Teleoperators and Virtual Environments have seen much discussion of what makes virtual environments seem real (see, e.g., Slater, 1999; Slater et al. 1994; Sheridan, 1992, 2000). Stephen Ellis, when organizing the meeting that motivated this paper, suggested to invited authors that "We may adopt as an organizing principle for the meeting that the genesis of apparently intelligent interaction arises from an upwelling of constraints determined by a hierarchy of lower levels of behavioral interaction. "My first reaction was "huh?" and my second was "yeah, that seems to make sense." Accordingly the paper seeks to explain from the author s viewpoint, why Ellis s hypothesis makes sense. What is the connection of "presence" or "immersion" of an observer in a virtual environment, to "constraints" and what types of constraints. What of "intelligent interaction," and is it the intelligence of the observer or the intelligence of the environment (whatever the latter may mean) that is salient? And finally, what might be relevant about "upwelling" of constraints as determined by a hierarchy of levels of interaction?

Sheridan, Thomas B.↗
DOE OSTI2025

PDPTW-DB: MILP-Based Offline Route Planning for PDPTW with Driver Breaks

The Pickup and Delivery Problem with Time Windows (PDPTW) involves optimizing routes for vehicles to meet pickup and delivery requests within specific time constraints, a challenge commonly faced in logistics and transportation. Microtransit, a flexible and demand-responsive service using smaller vehicles within defined zones, can be effectively modeled as a PDPTW. Yet, the need for driver breaks—a key human constraint—is frequently overlooked in PDPTW solutions, despite being necessary for regulatory compliance. This study presents a novel mixed-integer linear programming formulation for the Pickup and Delivery Problem with Time Windows and Driver Breaks (PDPTW-DB). To the best of our knowledge this formulation is the first to consider mandatory periodic driver breaks within optimized Microtransit routes. The proposed model incorporates regulatory compliant break scheduling directly within the vehicle routing optimization framework. By considering driver break requirements as an integral component of the optimization process, rather than as a post-processing step, the model enables the generation of routes that respect hours of service regulations while minimizing operational costs. This integrated approach facilitates the generation of schedules that are operationally efficient and prioritize driver welfare through driver breaks. We work with a public transit agency from the southern USA, and highlight the specific nuances of driver break optimization, and present a Pickup and Delivery Problem with Time Windows formulation for optimizing Microtransit operations and scheduling driver breaks. We validate our approach using real-world data from the transit agency. Our results validate our formulation in producing cost-effective, and regulation-compliant solutions.

Applied Computing, Transportation↗
DOE OSTI2022

The SPAN512 mid-latitude pulsar survey at the Nançay Radio Telescope

The large number of ongoing surveys for pulsars and transients at various radio observatories is motivated by the science obtained from these sources. Timing and polarisation analysis of relativistic binaries can place strong constraints on theories of gravity. The observation of a growing number of millisecond pulsars (MSPs) spread over the celestial sphere may allow the detection of a stochastic gravitational wave background arising from supermassive black hole binaries. A more complete sample of young pulsars improves our knowledge of neutron star birth and evolution. Transients such as fast radio bursts can serve to probe the intergalactic medium. The SPAN512 pulsar survey covers intermediate Galactic latitudes using the L-band receiver of the Nançay Radio Telescope (NRT). The survey covers 224 sq. deg. of the sky for a total exposure time of 2200 h. Population syntheses predict the discovery of 3 to 19 new normal pulsars and a few MSPs. We present detailed modelling of the NRT beam with its L-band receiver and its sensitivity which we used to precisely assess the expected survey yield. We used the flexible Pulsar Arecibo L-band Feed Array data processing pipeline to search the 47 TB of SPAN512 data for pulsars and transients. The SPAN512 survey discovered two new MSPs and one new middle-aged pulsar. We focus on the analysis of the 2.4-ms spin period pulsar J2205+6012 for which we also report the detection of gamma-ray pulsations. Its narrow pulse width (35µs at an observing frequency of 2.55 GHz) allows for sub-microsecond timing precision over 8 yr, with exciting prospects for pulsar timing array programs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Integrated Design of Ultradurable, Low CO 2 Alternative Binder Systems via Machine Learning

This ARPA-E project developed a machine learning tool to use in formulation design of cementitious binders for concrete having 50% less embodied CO 2 and possessing twice the durability compared to concrete based on ordinary portland cement (OPC) binders. The technical focus was on limestone/calcined clay cement (LC3), the leading replacement for OPC. Here, hierarchical machine learning (HML) was used to model the flowability, set time, strength, and durability of LC3 concrete. This methodology identifies latent variables derived from domain knowledge and empirical models that develop an accurate model for a response surface from small datasets. For the flowability metric, particle packing was a dominant factor, while strength and durability were both strongly determined by the fraction of metakaolin and the water:solids ratio. Under constraints of water:binder ratio, material performance metrics, embodied CO 2 , and cost per tonne of OPC, multi-objective optimization was used to design binders parameterized by the mineral composition replacing OPC, particle size distributions, and water:solids ratio. The trained algorithm was able to predict multiple mixes met these performance criteria, and experimental testing validated the predictions. The model demonstrated here is relevant for North America, where pure kaolin deposits are found broadly. The approach is being taken forward into commercial application by Ansatz AI, a materials informatics company founded by PI Washburn and co-PI Poczos. Through collaborations with the cement and concrete industry, and funding from SBIR programs, a commercial software will be developed in future research.

36 MATERIALS SCIENCE↗
NASA NTRS2014

UAS Applications for Hurricane Science, Hurrican and Severe Storm Sentinel (HS3)

Earth Science Industry Update: UAS Applications for Hurricane Science Unmanned systems can significantly transform hurricane observations and monitoring, improving our knowledge about and ability to forecast storm formation, track, and intensity change. NASA's use of the Global Hawk has demonstrated the scientific value of this platform and provided a proof-of-concept for operational applications. However, science flight operations face several challenges and constraints. In this session, learn about how NASA adapted the Global Hawk to do science; How NASA conducts its hurricane missions, and some of the challenges and constraints they face; Science results from NASA's recent hurricane field campaigns using the Global Hawk. How assimilation of dropsonde and radar data into weather prediction models may improve forecast accuracy; Other Earth science problems that could be addressed with Global Hawks.

Hurricane Science↗
NASA NTRS2004

An Enabling Technology for New Planning and Scheduling Paradigms

The Night Projects Directorate at NASA's Marshall Space Flight Center is developing a new planning and scheduling environment and a new scheduling algorithm to enable a paradigm shift in planning and scheduling concepts. Over the past 33 years Marshall has developed and evolved a paradigm for generating payload timelines for Skylab, Spacelab, various other Shuttle payloads, and the International Space Station. The current paradigm starts by collecting the requirements, called ?ask models," from the scientists and technologists for the tasks that are to be scheduled. Because of shortcomings in the current modeling schema, some requirements are entered as notes. Next, a cadre with knowledge of vehicle and hardware modifies these models to encompass and be compatible with the hardware model; again, notes are added when the modeling schema does not provide a better way to represent the requirements. Finally, the models are modified to be compatible with the scheduling engine. Then the models are submitted to the scheduling engine for automatic scheduling or, when requirements are expressed in notes, the timeline is built manually. A future paradigm would provide a scheduling engine that accepts separate science models and hardware models. The modeling schema would have the capability to represent all the requirements without resorting to notes. Furthermore, the scheduling engine would not require that the models be modified to account for the capabilities (limitations) of the scheduling engine. The enabling technology under development at Marshall has three major components: (1) A new modeling schema allows expressing all the requirements of the tasks without resorting to notes or awkward contrivances. The chosen modeling schema is both maximally expressive and easy to use. It utilizes graphical methods to show hierarchies of task constraints and networks of temporal relationships. (2) A new scheduling algorithm automatically schedules the models without the intervention of a scheduling expert. The algorithm is tuned for the constraint hierarchies and the complex temporal relationships provided by the modeling schema. It has an extensive search algorithm that can exploit timing flexibilities and constraint and relationship options. (3) An innovative architecture allows multiple remote users to simultaneously model science and technology requirements and other users to model vehicle and hardware characteristics. The architecture allows the remote users to submit scheduling requests directly to the scheduling engine and immediately see the results. These three components are integrated so that science and technology experts with no knowledge of the vehicle or hardware subsystems and no knowledge of the internal workings of the scheduling engine have the ability to build and submit scheduling requests and see the results. The immediate feedback will hone the users' modeling skills and ultimately enable them to produce the desired timeline. This paper summarizes the three components of the enabling technology and describes how this technology would make a new paradigm possible.

Jaap, John↗
NASA NTRS2014

Interdisciplinary Interactions During R&D and Early Design of Large Engineered Systems

Designing Large-Scale Complex Engineered Systems (LaCES) such as aircraft and submarines requires the input of thousands of engineers and scientists whose work is proximate in neither time nor space. Comprehensive knowledge of the system is dispersed among specialists whose expertise is in typically one system component or discipline. This study examined the interactive work practices among such specialists seeking to improve engineering practice through a rigorous and theoretical understanding of current practice. This research explored current interdisciplinary practices and perspectives during R&D and early LaCES design and identified why these practices and perspectives prevail and persist. The research design consisted of a three-fold, integrative approach that combined an open-ended survey, semi-structured interviews, and ethnography. Significant empirical data from experienced engineers and scientists in a large engineering organization were obtained and integrated with theories from organization science and engineering. Qualitative analysis was used to obtain a holistic, contextualized understanding. The over-arching finding is that issues related to cognition, organization, and social interrelations mostly dominate interactions across disciplines. Engineering issues, such as the integration of hardware or physics-based models, are not as significant. For example, organization culture is an important underlying factor that guided researchers more toward individual sovereignty over cross-disciplinarity. The organization structure and the engineered system architecture also serve as constraints to the engineering work. Many differences in work practices were observed, including frequency and depth of interactions, definition or co-construction of requirements, clarity or creation of the system architecture, work group proximity, and cognitive challenges. Practitioners are often unaware of these differences resulting in confusion and incorrect assumptions regarding work expectations. Cognitively, the enactment and coconstruction of knowledge are the fundamental tasks of the interdisciplinary interactions. Distributed and collective cognition represent most of the efforts. Argument, ignorance, learning, and creativity are interrelated aspects of the interactions that cause discomfort but yield benefits such as problem mitigation, broader understanding, and improved system design and performance. The quality and quantity of social interrelations are central to all work across disciplines with reciprocity, respectful engagement, and heedful interrelations being significant to the effectiveness of the engineering and scientific work.

McGowan, Anna-Maria Rivas↗
NASA NTRS2013

Multiple-Frame Detection of Subpixel Targets in Thermal Image Sequences

The new technology in this approach combines the subpixel detection information from multiple frames of a sequence to achieve a more sensitive detection result, using only the information found in the images themselves. It is taken as a constraint that the method is automated, robust, and computationally feasible for field networks with constrained computation and data rates. This precludes simply downloading a video stream for pixel-wise co-registration on the ground. It is also important that this method not require precise knowledge of sensor position or direction, because such information is often not available. It is also assumed that the scene in question is approximately planar, which is appropriate for a high-altitude airborne or orbital view.

Thompson, David R.↗
NASA NTRS2004

Enabling a New Planning and Scheduling Paradigm

The Flight Projects Directorate at NASA's Marshall Space Flight Center is developing a new planning and scheduling environment and a new scheduling algorithm to enable a paradigm shift in planning and scheduling concepts. Over the past 33 years Marshall has developed and evolved a paradigm for generating payload timelines for Skylab, Spacelab, various other Shuttle payloads, and the International Space Station. The current paradigm starts by collecting the requirements, called "tasks models," from the scientists and technologists for the tasks that they want to be done. Because of shortcomings in the current modeling schema, some requirements are entered as notes. Next a cadre with knowledge of vehicle and hardware modifies these models to encompass and be compatible with the hardware model; again, notes are added when the modeling schema does not provide a better way to represent the requirements. Finally, another cadre further modifies the models to be compatible with the scheduling engine. This last cadre also submits the models to the scheduling engine or builds the timeline manually to accommodate requirements that are expressed in notes. A future paradigm would provide a scheduling engine that accepts separate science models and hardware models. The modeling schema would have the capability to represent all the requirements without resorting to notes. Furthermore, the scheduling engine would not require that the models be modified to account for the capabilities (limitations) of the scheduling engine. The enabling technology under development at Marshall has three major components. (1) A new modeling schema allows expressing all the requirements of the tasks without resorting to notes or awkward contrivances. The chosen modeling schema is both maximally expressive and easy to use. It utilizes graphics methods to show hierarchies of task constraints and networks of temporal relationships. (2) A new scheduling algorithm automatically schedules the models without the intervention of a scheduling expert. The algorithm is tuned for the constraint hierarchies and the complex temporal relationships provided by the modeling schema. It has an extensive search algorithm which can exploit timing flexibilities and constraint and relationship options. (3) A web-based architecture allows multiple remote users to simultaneously model science and technology requirements and other users to model vehicle and hardware characteristics. The architecture allows the users to submit scheduling requests directly to the scheduling engine and immediately see the results. These three components are integrated so that science and technology experts with no knowledge of the vehicle or hardware subsystems and no knowledge of the internal workings of the scheduling engine have the ability to build and submit scheduling requests and see the results. The immediate feedback will hone the users' modeling skills and ultimately enable them to produce the desired timeline. This paper summarizes the three components of the enabling technology and describes how this technology would make a new paradigm possible.

Jaap, John↗
NASA NTRS1998

A Globally Convergent Augmented Lagrangian Pattern Search Algorithm for Optimization with General Constraints and Simple Bounds

We give a pattern search adaptation of an augmented Lagrangian method due to Conn, Gould, and Toint. The algorithm proceeds by successive bound constrained minimization of an augmented Lagrangian. In the pattern search adaptation we solve this subproblem approximately using a bound constrained pattern search method. The stopping criterion proposed by Conn, Gould, and Toint for the solution of this subproblem requires explicit knowledge of derivatives. Such information is presumed absent in pattern search methods; however, we show how we can replace this with a stopping criterion based on the pattern size in a way that preserves the convergence properties of the original algorithm. In this way we proceed by successive, inexact, bound constrained minimization without knowing exactly how inexact the minimization is. So far as we know, this is the first provably convergent direct search method for general nonlinear programming.

Lewis, Robert Michael↗
DOE OSTI2026

Machine learning-enhanced MPC for demand flexibility in small commercial buildings: An experimental study

Small- and medium-sized commercial buildings (SMCBs) represent the majority of U.S. commercial building stock and a significant share of peak electricity demand, yet they often lack centralized building automation systems, representing a significant untapped resource for urban energy management. This infrastructure gap makes advanced control implementation challenging, limiting the potential for widespread demand flexibility. Model Predictive Control (MPC) has shown strong potential for load shifting, peak demand reduction, and cost savings, but its effectiveness is hindered by unmeasured disturbances such as internal heat gains. This paper presents a Hybrid MPC framework that integrates a physics-based gray-box building thermal model, identified using a lumped disturbance (LD) approach, with a machine learning (ML) model for forecasting unmeasured disturbances. The hybrid approach is designed for buildings with multiple individually controlled heat pump and thermostat pairs, common in SMCBs, and aims to optimize coordinated scheduling of multiple heat pumps under dynamic electricity pricing while respecting comfort constraints. The methodology is validated through both simulations of case study buildings and experimental studies at a highly-instrumented test facility. Simulation results show that the Hybrid MPC achieves substantial load shifting and peak demand reduction, approaching the performance of an ideal MPC with perfect disturbance knowledge, and outperforming a conventional MPC without disturbance forecasting. In experiments, the Hybrid MPC reduced daily HVAC energy costs by 8.7%, peak-price time load (load shifting) by 41.7%, and peak demand by 29.2% compared to baseline control, demonstrating comparable benefits to the 11.6% cost savings, 42.9% load shifting, and 23.2% peak reduction of the ideal MPC. These results demonstrate that the proposed hybrid modeling approach can significantly improve MPC performance in real-world SMCB applications without requiring additional disturbance measurements.

Demand Flexibility↗
NASA NTRS1984

Three dimensional global modeling of atmospheric CO2

The initial attempts to model the atmospheric CO2 distribution, including couplings to the ocean and biosphere as sources and sinks of atmospheric CO2, encourage the notion that this approach will lead to useful quantitative constraints on CO2 fluxes. Realization of this objective will require: (1) continued improvement in the realism of the global transport modeling; (2) extended timeline of atmospheric CO2 monitoring, which improved precision and improved definition of the uncertainties in the measured CO2 amounts; and (3) given an accurate knowledge of model capabilities and limitations and given a good understanding of CO2 observations and their limitations, there is a need for good ideas concerning what quantitative information on the carbon cycle can be inferred from global modeling.

Hanse, J.↗
NASA NTRS1993

Systems aspects of COBE science data compression

A general approach to compression of diverse data from large scientific projects has been developed and this paper addresses the appropriate system and scientific constraints together with the algorithm development and test strategy. This framework has been implemented for the COsmic Background Explorer spacecraft (COBE) by retrofitting the existing VAS-based data management system with high-performance compression software permitting random access to the data. Algorithms which incorporate scientific knowledge and consume relatively few system resources are preferred over ad hoc methods. COBE exceeded its planned storage by a large and growing factor and the retrieval of data significantly affects the processing, delaying the availability of data for scientific usage and software test. Embedded compression software is planned to make the project tractable by reducing the data storage volume to an acceptable level during normal processing.

Freedman, I.↗
NASA NTRS2001

Information Retrieval from SAGE II and MFRSR Multi-Spectral Extinction Measurements

Direct beam spectral extinction measurements of solar radiation contain important information on atmospheric composition in a form that is essentially free from multiple scattering contributions that otherwise tend to complicate the data analysis and information retrieval. Such direct beam extinction measurements are available from the solar occultation satellite-based measurements made by the Stratospheric and Aerosol Gas Experiment (SAGE II) instrument and by ground-based Multi-Filter Shadowband Radiometers (MFRSRs). The SAGE II data provide cross-sectional slices of the atmosphere twice per orbit at seven wavelengths between 385 and 1020 nm with approximately 1 km vertical resolution, while the MFRSR data provide atmospheric column measurements at six wavelengths between 415 and 940 nm but at one minute time intervals. We apply the same retrieval technique of simultaneous least-squares fit to the observed spectral extinctions to retrieve aerosol optical depth, effective radius and variance, and ozone, nitrogen dioxide, and water vapor amounts from the SAGE II and MFRSR measurements. The retrieval technique utilizes a physical model approach based on laboratory measurements of ozone and nitrogen dioxide extinction, line-by-line and numerical k-distribution calculations for water vapor absorption, and Mie scattering constraints on aerosol spectral extinction properties. The SAGE II measurements have the advantage of being self-calibrating in that deep space provides an effective zero point for the relative spectral extinctions. The MFRSR measurements require periodic clear-day Langley regression calibration events to maintain accurate knowledge of instrument calibration.

Lacis, Andrew A.↗
DOE OSTI2025

A grid-scale study of demand bidding by large industrial users

A demand bidding mechanism for engaging large industrial electricity users in the operation of the power grid is presented. Demand bidding is formulated as an optimization problem based on a modified version of the alternating current optimal power flow problem, and can be interpreted as a tâtonnement process between the grid operator and electricity users. Here, the work provides the first – to the authors’ knowledge – grid-scale case study of demand bidding, using a synthetic grid structure in the footprint of the grid of Texas. Results reveal that the demand bidding lowers overall power generation costs, but economic benefits plateau as the number of participants increases. Transmission line and transformer capacity constraints become the limiting factors, revealing that expanding and fortifying the transmission infrastructure is key to expanding demand-side participation. Demand bidding does not substantially alter the optimal operation of existing bidding entities when the number of bidders increases, thereby supporting existing bidders to stay in the system and encouraging new ones to join.

Chlor-alkali plant↗