SEARCH · Search NASA

Results for “Constraint Reduction”

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.

SourceResearch connectionFrom yearThrough year

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

At least 415 records · Page 23

All sources NASA NTRS DOE OSTI

DOE OSTI2016

T-duality constraints on higher derivatives revisited

Here, we ask to what extent are the higher-derivative corrections of string theory constrained by T-duality. The seminal early work by Meissner tests T-duality by reduction to one dimension using a distinguished choice of field variables in which the bosonic string action takes a Gauss-Bonnet-type form. By analyzing all field redefinitions that may or may not be duality covariant and may or may not be gauge covariant we extend the procedure to test T-duality starting from an action expressed in arbitrary field variables. We illustrate the method by showing that it determines uniquely the first-order α' corrections of the bosonic string, up to terms that vanish in one dimension. We also use the method to glean information about the O(α'2) corrections in the double field theory with Green-Schwarz deformation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Innovative rail transport of a supersized land-based wind turbine blade

Wind turbine blade logistic providers are being challenged with escalating costs and routing complexities as one-piece blade approach lengths of 75 m in various regions of the U.S. land-based market. New lower cost solutions are needed to enable further reductions in the levelized cost of energy (LCOE) and continued market expansion. In this paper, a novel method of using existing U.S. rail infrastructure to deploy 100-m, one-piece blades to U.S. land-based wind sites is numerically investigated. The study removes the constraint that blades must be kept rigid during transport, and it allows bending to keep blades within a clearance profile while navigating horizontal and vertical curvatures. Novel system optimization and blade design processes consider blade structural constraints and rail logistic constraints in parallel to develop a highly flexible, rail-transportable blade. Results indicate maximum deployment potential in the Interior region of the United States and limited deployment potential in other regions. The study concludes that innovative rail transportation solutions combined with advanced rotor technologies can provide a feasible alternative to segmentation and support continued LCOE reductions in the U.S. land-based wind energy market.

17 WIND ENERGY↗

DOE OSTI2015

Pulmonary nodule detection in CT images based on shape constraint CV model

Purpose: Accurate detection of pulmonary nodules remains a technical challenge in computer-aided diagnosis systems because some nodules may adhere to the blood vessels or the lung wall, which have low contrast compared to the surrounding tissues. In this paper, the analysis of typical shape features of candidate nodules based on a shape constraint Chan–Vese (CV) model combined with calculation of the number of blood branches adhered to nodule candidates is proposed to reduce false positive (FP) nodules from candidate nodules. Methods: The proposed scheme consists of three major stages: (1) Segmentation of lung parenchyma from computed tomography images. (2) Extraction of candidate nodules. (3) Reduction of FP nodules. A gray level enhancement combined with a spherical shape enhancement filter is introduced to extract the candidate nodules and their sphere-like contour regions. FPs are removed by analysis of the typical shape features of nodule candidates based on the CV model using spherical constraint and by investigating the number of blood branches adhered to the candidate nodules. The constrained shapes of CV model are automatically achieved from the extracted candidate nodules. Results: The detection performance was evaluated on 127 nodules of 103 cases including three types of challenging nodules, which are juxta-pleural nodules, juxta-vascular nodules, and ground glass opacity nodules. The free-receiver operating characteristic (FROC) curve shows that the proposed method is able to detect 88% of all the nodules in the data set with 4 FPs per case. Conclusions: Evaluation shows that the authors’ method is feasible and effective for detection of three types of nodules in this study.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Exploring the role of 𝑑* hexaquarks on quark deconfinement and hybrid stars

We investigate the impact of the d*( 2380) hexaquark on the equation of state (EOS) of dense matter within hybrid stars (HSs) using the chiral mean-field model (CMF). The hexaquark is included as a new degree of freedom in the hadronic phase, and its influence on the deconfinement transition to quark matter is explored. We reparametrize the CMF model to ensure compatibility with recent astrophysical constraints, including the observation of massive pulsars and gravitational wave events. Our results show that the presence of d* significantly modifies the EOS, leading to a softening at high densities and a consequent reduction in the predicted maximum stellar masses. Furthermore, we examine the possibility of a first-order deconfinement phase transition within the context of the extended stability branch of slow stable HSs (SSHSs). We find that the presence of hexaquarks can delay the deconfinement phase transition and reduce the associated energy density gap, affecting the structure and stability of HSs. Our results suggest that, as the hexaquark appearance tends to destabilize stellar configurations, fine-tuning of model parameters is required to obtain both the presence of hexaquarks and quark deconfinement in these systems. In this scenario, the SSHS branch plays a crucial role in obtaining HSs with hexaquarks that satisfy current astrophysical constraints. Our work provides new insights into the role of exotic particles like d* in dense matter and the complex interplay between hadronic and quark degrees of freedom inside compact stellar objects.

Nuclear astrophysics↗

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↗

DOE OSTI2015

Exploring the parameter space of warm-inflation models

Warm inflation includes inflaton interactions with other fields throughout the inflationary epoch instead of confining such interactions to a distinct reheating era. Previous investigations have shown that, when certain constraints on the dynamics of these interactions and the resultant radiation bath are satisfied, a low-momentum-dominated dissipation coefficient ∝T{sup 3}/m{sub χ}{sup 2} can sustain an era of inflation compatible with CMB observations. In this work, we extend these analyses by including the pole-dominated dissipation term ∝√(m{sub χ}T)exp (−m{sub χ}/T). We find that, with this enhanced dissipation, certain models, notably the quadratic hilltop potential, perform significantly better. Specifically, we can achieve 50 e-folds of inflation and a spectral index compatible with Planck data while requiring fewer mediator field (O(10{sup 4}) for the quadratic hilltop potential) and smaller coupling constants, opening up interesting model-building possibilities. We also highlight the significance of the specific parametric dependence of the dissipative coefficient which could prove useful in even greater reduction in field content.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

Holistic Approaches to HPC Power and Workflow Management

Constraints on power consumption are having pervasive effects on high-performance computing (HPC) systems, the facilities in which they are housed, and the application codes themselves. Whether these constraints are driven by physical limits on available power within a facility, are due to utility demand response, result from opportunities to reduce utility costs, or are imposed by sponsoring agencies, power management must now be added to the traditional goals of application and algorithm correctness, scalability, and performance. The specifics of these power constraints are likely to vary from site to site, and optimal strategies for managing power will vary. In this paper, we present the results of implementing several strategies for managing HPC system power and quantify the impact of these power reductions on application performance. These can be added to the suite of extant options that can be used to manage power and workflow on HPC systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Holistic Approaches to HPC Power & Workflow Management: Preprint

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Neutron star mass-radius constraints of the quiescent low-mass X-ray binaries X7 and X5 in the globular cluster 47 Tuc

We present Chandra/ACIS-S subarray observations of the quiescent neutron star (NS) low-mass X-ray binaries X7 and X5 in the globular cluster 47 Tuc. The large reduction in photon pile-up compared to previous deep exposures enables a substantial improvement in the spectroscopic determination of the NS radius and mass of these NSs. Modeling the thermal emission from the NS surface with a non-magnetized hydrogen atmosphere and accounting for numerous sources of uncertainties, we obtain for the NS in X7 a radius of R=11.1{sub −0.7}{sup +0.8} km for an assumed stellar mass of M = 1.4 M {sub ⊙} (68% confidence level). We argue, based on astrophysical grounds, that the presence of a He atmosphere is unlikely for this source. Due to the excision of data affected by eclipses and variable absorption, the quiescent low-mass X-ray binary X5 provides less stringent constraints, leading to a radius of R=9.6{sub −1.1}{sup +0.9} km, assuming a hydrogen atmosphere and a mass of M = 1.4 M {sub ⊙}. When combined with all existing spectroscopic radius measurements from other quiescent low-mass X-ray binaries and Type I X-ray bursts, these measurements strongly favor radii in the 9.9–11.2 km range for a ∼1.5 M {sub ⊙} NS and point to a dense matter equation of state that is somewhat softer than the nucleonic ones that are consistent with laboratory experiments at low densities.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Nuclear microreactor transient and load-following control with deep reinforcement learning

The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for real-time drum control in microreactors, exploring performance in regard to load-following scenarios. By leveraging a point kinetics model with thermal and xenon feedback, we first establish a baseline using a single-output RL agent, then compare it against a traditional proportional–integral–derivative (PID) controller. This study demonstrates that RL controllers, including both single- and multi-agent RL (MARL) frameworks, can achieve similar or even superior load-following performance as traditional PID control across a range of load-following scenarios. In short transients, the RL agent was able to reduce the tracking error rate in comparison to PID by one half to one third. Over extended 300-minute load-following scenarios in which xenon feedback becomes a dominant factor, PID maintained better accuracy, but RL still remained within a 1% error margin despite being trained only on short-duration scenarios. This highlights RL’s strong ability to generalize and extrapolate to longer, more complex transients, affording substantial reductions in training costs and reduced overfitting. Furthermore, when control was extended to multiple drums, MARL enabled independent drum control as well as maintained reactor symmetry constraints without sacrificing performance---an objective that standard single-agent RL could not learn. We also found that, as increasing levels of Gaussian noise were added to the power measurements, the RL controllers were able to maintain lower error rates than PID, and to do so with at least 10% and upwards of 150% less control effort. These findings illustrate RL's potential for autonomous nuclear reactor control, laying the groundwork for future integration into high-fidelity simulations and experimental validation efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1987

Design sensitivity analysis of rotorcraft airframe structures for vibration reduction

Optimization of rotorcraft structures for vibration reduction was studied. The objective of this study is to develop practical computational procedures for structural optimization of airframes subject to steady-state vibration response constraints. One of the key elements of any such computational procedure is design sensitivity analysis. A method for design sensitivity analysis of airframes under vibration response constraints is presented. The mathematical formulation of the method and its implementation as a new solution sequence in MSC/NASTRAN are described. The results of the application of the method to a simple finite element stick model of the AH-1G helicopter airframe are presented and discussed. Selection of design variables that are most likely to bring about changes in the response at specified locations in the airframe is based on consideration of forced response strain energy. Sensitivity coefficients are determined for the selected design variable set. Constraints on the natural frequencies are also included in addition to the constraints on the steady-state response. Sensitivity coefficients for these constraints are determined. Results of the analysis and insights gained in applying the method to the airframe model are discussed. The general nature of future work to be conducted is described.

Murthy, T. Sreekanta↗

NASA NTRS1990

Preliminary design optimization of joined-wing aircraft

The joined wing is an innovative aircraft configuration that has a its tail connected to the wing forming a diamond shape in both top and plan view. This geometric arrangement utilizes the tail for both pitch control and as a structural support for the wing. Several researchers have studied this configuration and predicted significant reductions in trimmed drag or structural weight when compared with a conventional T-tail configuration. Kroo et al. compared the cruise drag of joined wings with conventional designs of the same lifting-surface area and structural weight. This study showed an 11 percent reduction in cruise drag for the lifting system of a joined wing. Although this reduction in cruise drag is significant, a complete design study is needed before any economic savings can be claimed for a joined-wing transport. Mission constraints, such as runway length, could increase the wing area and eliminate potential drag savings. Since other design codes do not accurately represent the interaction between structures and aerodynamics for joined wings, we developed a new design code for this study. The aerodynamic and structural analyses in this study are significantly more sophisticated than those used in most conventional design codes. This sophistication was needed to predict the aerodynamic interference between the wing and tail and the stresses in the truss-like structure. This paper describes these analysis methods, discusses some problems encountered when applying the numerical optimizer NPSOL, and compares optimum joined wings with conventional aircraft on the basis of cruise drag, lifting surface weight, and direct operating cost (DOC).

Gallman, John W.↗

NASA NTRS2003

High Power Laser Diode Arrays for 2-Micron Solid State Coherent Lidars Applications

Laser diode arrays are critical components of any diode-pumped solid state laser systems, constraining their performance and reliability. Laser diode arrays (LDAs) are used as the pump source for energizing the solid state lasing media to generate an intense coherent laser beam with a high spatial and spectral quality. The solid state laser design and the characteristics of its lasing materials define the operating wavelength, pulse duration, and power of the laser diodes. The pump requirements for high pulse energy 2-micron solid state lasers are substantially different from those of more widely used 1-micron lasers and in many aspects more challenging [1]. Furthermore, the reliability and lifetime demanded by many coherent lidar applications, such as global wind profiling from space and long-range clear air turbulence detection from aircraft, are beyond the capability of currently available LDAs. In addition to the need for more reliable LDAs with longer lifetime, further improvement in the operational parameters of high power quasi-cw LDAs, such as electrical efficiency, brightness, and duty cycle, are also necessary for developing cost-effective 2-micron coherent lidar systems for applications that impose stringent size, heat dissipation, and power constraints. Global wind sounding from space is one of such applications, which is the main driver for this work as part of NASA s Laser Risk Reduction Program. This paper discusses the current state of the 792 nm LDA technology and the technology areas being pursued toward improving their performance. The design and development of a unique characterization facility for addressing the specific issues associated with the LDAs for pumping 2-micron coherent lidar transmitters and identifying areas of technological improvement will be described. Finally, the results of measurements to date on various standard laser diode packages, as well as custom-designed packages with potentially longer lifetime, will be reported.

Amzajerdian, Farzin↗

NASA NTRS2017

Nano Icy Moons Propellant Harvester

As one of just a few bodies identified in the solar system with a liquid ocean, Europa has become a top priority in the search for life outside of Earth. However, cost estimates for exploring Europa have been prohibitively expensive, with estimates of a NASA Flagship class orbiter and lander approaching $5 billion. ExoTerra's NIMPH offers an affordable solution that can not only land, but return a sample from the surface to Earth. NIMPH combines solar electric propulsion (SEP) technologies being developed for the asteroid redirect mission and microsatellite electronics to reduce the cost of a full sample return mission below $500 million. A key to achieving this order-of-magnitude cost reduction is minimizing the initial mass of the system. The cost of any mission is directly proportional to its mass. By keeping the mission within the constraints of an Atlas V 551 launch vehicle versus an SLS, we can significantly reduce launch costs. To achieve this we reduce the landed mass of the sample return lander, which is the largest multiplier of mission mass, and shrink propellant mass through high efficiency SEP and gravity assists. The NIMPH projects first step in reducing landed mass focuses on development of a micro-In Situ Resource Utilization (micro-ISRU) system. ISRU allows us to minimize landed mass of a sample return mission by converting local ice into propellants. The project reduces the ISRU system to a CubeSat-scale package that weighs just 1.74 kg and consumes just 242 W of power. We estimate that use of this ISRU vs. an identical micro-lander without ISRU reduces fuel mass by 45 kg. As the dry mass of the lander grows for larger missions, these savings scale exponentially. Taking full advantage of the micro-ISRU system requires the development of a micro-liquid oxygen-liquid hydrogen engine. The micro-liquid oxygen-liquid hydrogen engine is tailored for the mission by scaling it to match the scale of the micro-lander and the low gravity of the target moon. We also tailor the engine for a near stoichiometric mixture ratio of 7.5. Most high-performance liquid oxygen-liquid hydrogen engines inject extra liquid hydrogen to lower the average molecular weight of the exhaust, which improves specific impulse. However, this extra liquid hydroden requires additional power and processing time on the surface for the ISRU to create. This increases mission cost, and on missions within high radiation environments such as Europa, increases radiation shielding mass. The resulting engine weighs just 1.36 kg and produces 71.5 newton of thrust at 364 s specific impulse. Finally, the mission reduces landed mass by taking advantage of the SEP modules solar power to beam energy to the surface using a collimated laser. This allows us to replace an 45 kg MMRTG with a 2.5 kg resonant array. By using the combination of ISRU, a liquid oxygen-liquid hydrogen engine, and beamed power, we reduce the initial mass of the lander to just 51.5 kg. When combined with an SEP module to ferry the lander to Europa the initial mission mass is just 6397 kg - low enough to be placed on an Earth escape trajectory using an Atlas V 551 launch vehicle. By comparison, we estimate a duplicate lander using an MMRTG and semi-storable propellants such as liquid oxygen-methane would result in an order of magnitude increase in initial lander mass to 445 kg. Attempting to perform the trajectory with a 450 s liquid oxygen-liquid hydrogen engine would increase initial mass to approximately 135,000 kg. Using an Atlas V 1 U.S. Dollar per kg rate to Earth escape value of $27.7k per kg, just the launch savings are over $3.5 billion.

liquid↗

DOE OSTI2016

Predicting future uncertainty constraints on global warming projections

Projections of global mean temperature changes (ΔT) in the future are associated with intrinsic uncertainties. Much climate policy discourse has been guided by "current knowledge" of the ΔTs uncertainty, ignoring the likely future reductions of the uncertainty, because a mechanism for predicting these reductions is lacking. By using simulations of Global Climate Models from the Coupled Model Intercomparison Project Phase 5 ensemble as pseudo past and future observations, we estimate how fast and in what way the uncertainties of ΔT can decline when the current observation network of surface air temperature is maintained. At least in the world of pseudo observations under the Representative Concentration Pathways (RCPs), we can drastically reduce more than 50% of the ΔTs uncertainty in the 2040 s by 2029, and more than 60% of the ΔTs uncertainty in the 2090 s by 2049. Under the highest forcing scenario of RCPs, we can predict the true timing of passing the 2°C (3°C) warming threshold 20 (30) years in advance with errors less than 10 years. These results demonstrate potential for sequential decision-making strategies to take advantage of future progress in understanding of anthropogenic climate change.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1996

The Influences of Lamination Angles on the Interior Noise Levels of an Aircraft

The feasibility of reducing the interior noise levels of an aircraft passenger cabin through optimization of the composite lay up of the fuselage is investigated. MSC/NASTRAN, a commercially available finite element code, is used to perform the dynamic analysis and subsequent optimization of the fuselage. The numerical calculation of sensitivity of acoustic pressure to lamination angle is verified using a simple thin, cylindrical shell with point force excitations as noise sources. The thin shell used represents a geometry similar to the fuselage and analytic solutions are available for the cylindrical thin shell equations of motion. Optimization of lamination angle for the reduction of interior noise is performed using a finite element model of an actual aircraft fuselage. The aircraft modeled for this study is the Beech Starship. Point forces simulate the structure borne noise produced by the engines and are applied to the fuselage at the wing mounting locations. These forces are the noise source for the optimization problem. The acoustic pressure response is reduced at a number of points in the fuselage and over a number of frequencies. The objective function is minimized with the constraint that it be larger than the maximum sound pressure level at the response points in the passenger cabin for all excitation frequencies in the range of interest. Results from the study of the fuselage model indicate that a reduction in interior noise levels is possible over a finite frequency range through optimal configuration of the lamination angles in the fuselage. Noise reductions of roughly 4 dB were attained. For frequencies outside the optimization range, the acoustic pressure response may increase after optimization. The effects of changing lamination angle on the overall structural integrity of the airframe are not considered in this study.

Fernholz, Christian M.↗

DOE OSTI2021

Resilience-Oriented DG Siting and Sizing Considering Stochastic Scenario Reduction

In this paper, a fuel-based distributed generator (DG) allocation strategy is proposed to enhance the distribution system resilience against extreme weather. The long-term planning problem is formulated as a two-stage stochastic mixed-integer programming (SMIP). The first stage is to make decisions of DG siting and sizing under the given budget constraint. In the second stage, a post-extreme-event-restoration (PEER) is employed to minimize the operating cost in an uncertain fault scenario. In particular, this study proposes a method to select the most representative scenarios for the SMIP. First, a Monte Carlo Simulation (MCS) is introduced to generate sufficient scenarios considering random fault locations and load profiles. Then, the number of scenarios is reduced by the K-means clustering algorithm. The advantage of scenario reduction is to make a trade-off between accuracy and computational efficiency. Finally, the SMIP is solved by the progressive hedging algorithm. Here, the case studies of the IEEE 33-bus and 123-bus test systems demonstrate the effectiveness of the proposed algorithm in reducing the expected energy not served (EENS), which is a critical criterion of resilience.

42 ENGINEERING↗

DOE OSTI2024

Modeling performance of data collection systems for high-energy physics

Exponential increases in scientific experimental data are outpacing silicon technology progress, necessitating heterogeneous computing systems—particularly those utilizing machine learning (ML)—to meet future scientific computing demands. The growing importance and complexity of heterogeneous computing systems require systematic modeling to understand and predict the effective roles for ML. We present a model that addresses this need by framing the key aspects of data collection pipelines and constraints and combining them with the important vectors of technology that shape alternatives, computing metrics that allow complex alternatives to be compared. For instance, a data collection pipeline may be characterized by parameters such as sensor sampling rates and the overall relevancy of retrieved samples. Alternatives to this pipeline are enabled by development vectors including ML, parallelization, advancing CMOS, and neuromorphic computing. By calculating metrics for each alternative such as overall F1 score, power, hardware cost, and energy expended per relevant sample, our model allows alternative data collection systems to be rigorously compared. We apply this model to the Compact Muon Solenoid experiment and its planned high luminosity-large hadron collider upgrade, evaluating novel technologies for the data acquisition system (DAQ), including ML-based filtering and parallelized software. The results demonstrate that improvements to early DAQ stages significantly reduce resources required later, with a power reduction of 60% and increased relevant data retrieval per unit power (from 0.065 to 0.31 samples/kJ). However, we predict that further advances will be required in order to meet overall power and cost constraints for the DAQ.

Olin-Ammentorp, Wilkie (ORCID:0000000224729862)↗