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At least 91 records · Page 5

Real-time fuzzy inference based robot path planning

This project addresses the problem of adaptive trajectory generation for a robot arm. Conventional trajectory generation involves computing a path in real time to minimize a performance measure such as expended energy. This method can be computationally intensive, and it may yield poor results if the trajectory is weakly constrained. Typically some implicit constraints are known, but cannot be encoded analytically. The alternative approach used here is to formulate domain-specific knowledge, including implicit and ill-defined constraints, in terms of fuzzy rules. These rules utilize linguistic terms to relate input variables to output variables. Since the fuzzy rulebase is determined off-line, only high-level, computationally light processing is required in real time. Potential applications for adaptive trajectory generation include missile guidance and various sophisticated robot control tasks, such as automotive assembly, high speed electrical parts insertion, stepper alignment, and motion control for high speed parcel transfer systems.

Pacini, Peter J.↗

Advocating for view and daylight in buildings: Next steps

With the exponential growth in population and commensurate increased density in urban cities, access to daylight and views to nature is being severely curtailed in buildings. In parallel, increasingly urgent demands to sharply reduce building energy use and associated greenhouse gas emissions are being made to mitigate climate change. There are many challenges and performance tradeoffs associated with the building facade (i.e., daylight and view versus solar and glare control); increased prioritization of health and well-being as a fundamental human requirement could adversely affect building energy-efficiency. Given the current state of knowledge on the effects of daylight and view on health and well-being in buildings, we identify critical needs in research, tools and technologies that if satisfied may enable more effective use of daylight and view in buildings within the constraints of climate change. Lack of knowledge regarding the complex causal mechanisms of window views on human factors is a severely limiting factor in forward progress. Current models and methods to derive bidirectional scattering distribution functions (BSDFs) will need to be modified. Developers of energy-efficient window technologies will need more guidance to shape product development. Advanced window technologies and integrated design can enable attainment of both health and well-being and net zero energy goals, but considerable work will be needed to make such options turnkey and broadly available.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Uncertainty management by relaxation of conflicting constraints in production process scheduling

Mathematical-analytical methods as used in Operations Research approaches are often insufficient for scheduling problems. This is due to three reasons: the combinatorial complexity of the search space, conflicting objectives for production optimization, and the uncertainty in the production process. Knowledge-based techniques, especially approximate reasoning and constraint relaxation, are promising ways to overcome these problems. A case study from an industrial CIM environment, namely high-grade steel production, is presented to demonstrate how knowledge-based scheduling with the desired capabilities could work. By using fuzzy set theory, the applied knowledge representation technique covers the uncertainty inherent in the problem domain. Based on this knowledge representation, a classification of jobs according to their importance is defined which is then used for the straightforward generation of a schedule. A control strategy which comprises organizational, spatial, temporal, and chemical constraints is introduced. The strategy supports the dynamic relaxation of conflicting constraints in order to improve tentative schedules.

Dorn, Juergen↗

Geophysical Constraints on Decarbonized Systems—Building Spatio-Temporal Uncertainties into Future Electricity Grid Planning

Future electricity grids will be characterized by the high penetration of renewables to support the decarbonization process. Yet, this transition will further expose grids to a broad spectrum of geophysical forces, such as weather and climate, land and space, minerals and materials. Understanding their role is thus important to support the grid decarbonization process. Here, we synthesize the current body of knowledge on the relationship between geophysical constraints and electricity grid planning. Additionally, we show that there have been promising advances in the data, methods, and modelling tools needed to incorporate the effect of geophysical constraints on demand, resource availability, and grid operations. There are, however, multiple research avenues that deserve further attention. More system-specific and finer-scale analyses are necessary to better understand how spatio-temporal variability---and associated uncertainty---in geophysical forces affect grid planning. Moreover, we need a broader focus on the multi-sectoral implications of grid decarbonization efforts, so as to limit the risk of unintended consequences. Importantly, all these efforts are challenged by the computational requirements of existing power system models, which often limit our ability to characterize uncertainty, scale analyses across larger domains, or study the multi-sector implications of grid expansion decisions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Constraint-based evaluation of sequential procedures

Constraining the operation of an agent requires knowledge of the restrictions to physical and temporal capabilities of that agent, as well as an inherent understanding of the desires being processed by that agent. Usually a set of constraints are available that must be adhered to in order to foster safe operations. In the worst case, violation of a constraint may be cause to terminate operation. If the agent is carrying out a plan, then a method for predicting the agent's desires, and therefore possible constraint violations, is required. The conceptualization of constraint-based reasoning used herein assumes that a system knows how to select a constraint for application as well as how to apply that constraint once it is selected. The application of constraint-based reasoning for evaluating certain kinds of plans known as sequential procedures is discussed. By decomposing these plans, it is possible to apply context dependent constraints in production system fashion without incorporating knowledge of the original planning process.

Barry, Matthew R.↗

Constraints on Mantle Plume Melting Conditions in the Martian Mantle Based on Improved Melting Phase Relationships of Olivine-Phyric Shergottite Yamato 980459

Martian meteorite Yamato 980459 (hereafter Y98) is an olivine-phyric shergottite that has been interpreted as closely approximating a martian mantle melt [1-4], making it an important constraint on adiabatic decompression melting models. It has long been recognized that low pressure melting of the Y98 composition occurs at extremely high temperatures relative to martian basalts (1430 degC at 1 bar), which caused great difficulties in a previous attempt to explain Y98 magma generation via a mantle plume model [2]. However, previous studies of the phase diagram were limited to pressures of 2 GPa and less [2, 5], whereas decompression melting in the present-day martian mantle occurs at pressures of 3-7 GPa, with the shallow boundary of the melt production zone occurring just below the base of the thermal lithosphere [6]. Recent experimental work has now extended our knowledge of the Y98 melting phase relationships to 8 GPa. In light of this improved petrological knowledge, we are therefore reassessing the constraints that Y98 imposes on melting conditions in martian mantle plumes. Two recently discovered olivine- phyric shergottites, Northwest Africa (NWA) 5789 and NWA 6234, may also be primary melts from the martian mantle [7, 8]. However, these latter meteorites have not been the subject of detailed experimental petrology studies, so we focus here on Y98.

Kiefer, Walter S.↗

Constraining Venus and Mars Internal Structures from the Gravity Response to Atmospheric Loading

The gravity fields of celestial bodies that possess an atmosphere are periodically perturbed by the redistribution of fluid mass associated with the atmospheric dynamics. A component of this perturbation is due to the gravitational response of the body to the deformation of its surface induced by the atmospheric pressure loading. The magnitude of this effect depends on the relation between the loading and the response in terms of geopotential variations measured by the load Love numbers. In this work, we simulate and analyze the gravity field generated by the atmospheres of Venus and Mars by accounting for different models of their internal structure. By precisely characterizing the phenomena that drive the mass transportation in the atmosphere through general circulation models, we determine the effect of the interior structure on the response to the atmospheric loading. An accurate estimation of the time-varying gravity field, which measures the atmospheric contribution, may provide significant constraints on the interior structure through the measurement of the load Love numbers. A combined determination of tidal and load Love numbers would enhance our knowledge of the interior of planetary bodies, providing further geophysical constraints in the inversion of internal structure models.The gravity fields of celestial bodies that possess an atmosphere are periodically perturbed by the redistribution of fluid mass associated with the atmospheric dynamics. A component of this perturbation is due to the gravitational response of the body to the deformation of its surface induced by the atmospheric pressure loading. The magnitude of this effect depends on the relation between the loading and the response in terms of geopotential variations measured by the load Love numbers. In this work, we simulate and analyze the gravity field generated by the atmospheres of Venus and Mars by accounting for different models of their internal structure. By precisely characterizing the phenomena that drive the mass transportation in the atmosphere through general circulation models, we determine the effect of the interior structure on the response to the atmospheric loading. An accurate estimation of the time-varying gravity field, which measures the atmospheric contribution, may provide significant constraints on the interior structure through the measurement of the load Love numbers. A combined determination of tidal and load Love numbers would enhance our knowledge of the interior of planetary bodies, providing further geophysical constraints in the inversion of internal structure models.

Mars↗

ATOS-1: Designing the infrastructure for an advanced spacecraft operations system

The space industry has identified the need to use artificial intelligence and knowledge based system techniques as integrated, central, symbolic processing components of future mission design, support and operations systems. Various practical and commercial constraints require that off-the-shelf applications, and their knowledge bases, are reused where appropriate and that different mission contractors, potentially using different KBS technologies, can provide application and knowledge sub-modules of an overall integrated system. In order to achieve this integration, which we call knowledge sharing and distributed reasoning, there needs to be agreement on knowledge representations, knowledge interchange-formats, knowledge level communications protocols, and ontology. Research indicates that the latter is most important, providing the applications with a common conceptualization of the domain, in our case spacecraft operations, mission design, and planning. Agreement on ontology permits applications that employ different knowledge representations to interwork through mediators which we refer to as knowledge agents. This creates the illusion of a shared model without the constraints, both technical and commercial, that occur in centralized or uniform architectures. This paper explains how these matters are being addressed within the ATOS program at ESOC, using techniques which draw upon ideas and standards emerging from the DARPA Knowledge Sharing Effort. In particular, we explain how the project is developing an electronic Ontology of Spacecraft Operations and how this can be used as an enabling component within space support systems that employ advanced software engineering. We indicate our hope and expectation that the core ontology developed in ATOS, will permit the full development of standards for such systems throughout the space industry.

Poulter, K. J.↗

Refining localtype primordial non-Gaussianity: Sharpened bϕ constraints through bias expansion

Local-type primordial non-Gaussianity (PNG), predicted by many nonminimal models of inflation, creates a scale-dependent contribution to the power spectrum of large-scale structure tracers. Its amplitude is characterized by the product bϕfNLloc, where bϕ is an astrophysical parameter dependent on the properties of the tracer. However, bϕ exhibits significant secondary dependence on halo concentration and other astrophysical properties, which may bias and weaken the constraints on fNLloc. In this work, we demonstrate that incorporating knowledge of the relation between Lagrangian bias parameters and bϕ can significantly enhance PNG constraints. We employ the hybrid effective field theory approach at the field level and a linear regression model to seek a connection between the bias parameters and bϕ for halo and galaxy samples, constructed using the abacussummit simulation suite and mimicking the luminous red galaxies and quasistellar objects of the Dark Energy Spectroscopic Instrument survey. For the fixed-mass halo samples, our full bias model reduces the uncertainty by more than 70%, with most of that improvement coming from b∇, which we find to be an excellent proxy for concentration. For the galaxy samples, our model reduces the uncertainty on bϕ by 80% for all tracers. By adopting Lagrangian-bias informed priors on the parameter bϕ, future analyses can thus constrain fNLloc with less bias and smaller errors.

Hadzhiyska, Boryana↗

Exozodiacal Dust Workshop

The purpose of the workshop was to understand what effect circumstellar dust clouds will have on NASA's proposed Terrestrial Planet Finder (TPF) mission's ability to search for terrestrial-sized planets orbiting stars in the solar neighborhood. The workshop participants reviewed the properties of TPF, summarized what is known about the local zodiacal cloud and about exozodiacal clouds, and determined what additional knowledge must be obtained to help design TPF for maximum effectiveness within its cost constraint. Recommendations were made for ways to obtain that additional knowledge, at minimum cost. The workshop brought together approximately 70 scientists, from four different countries. The active participants included astronomers involved in the study of the local zodiacal cloud, in the formation of stars and planetary systems, and in the technologies and techniques of ground- and space-based infrared interferometry. During the course of the meeting, 15 invited talks and 20 contributed poster papers were presented, and there were four working sessions. This is a collection of the invited talks, contributed poster papers, and summaries of the working sessions.

Backman, D. E.↗

Optimal carbon storage reservoir management through deep reinforcement learning

Model-based optimization plays a central role in energy system design and management. The complexity and high-dimensionality of many process-level models, especially those used for geosystem energy exploration and utilization, often lead to formidable computational costs when the dimension of decision space is also large. This work adopts elements of recently advanced deep learning techniques to solve a sequential decision-making problem in applied geosystem management. Specifically, a deep reinforcement learning framework was formed for optimal multiperiod planning, in which a deep Q-learning network (DQN) agent was trained to maximize rewards by learning from high-dimensional inputs and from exploitation of its past experiences. To expedite computation, deep multitask learning was used to approximate high-dimensional, multistate transition functions. Both DQN and deep multitask learning are pattern based. As a demonstration, the framework was applied to optimal carbon sequestration reservoir planning using two different types of management strategies: monitoring only and brine extraction. Both strategies are designed to mitigate potential risks due to pressure buildup. Results show that the DQN agent can identify the optimal policies to maximize the reward for given risk and cost constraints. Finally, experiments also show that knowledge the agent gained from interacting with one environment is largely preserved when deploying the same agent in other similar environments.

15 GEOTHERMAL ENERGY↗

Hybrid real- and reciprocal-space full-field imaging with coherent illumination

In this work, we present a novel diffractive imaging method that harnesses a low-resolution real-space image to guide the phase retrieval. A computational algorithm is developed to utilise such prior knowledge as a real-space constraint in the iterative phase retrieval procedure. Numerical simulations and proof-of-concept experiments are carried out, demonstrating our method's capability of reconstructing high-resolution details that are otherwise inaccessible with traditional phasing algorithms. With the present method, we formulate a conceptual design for the coherent imaging experiments at a next-generation x-ray light source.

42 ENGINEERING↗

Electrochemical Reduction of Flue Gas Carbon Dioxide to Commercially Viable C2-C4 Products (Final Report)

This is the final scientific/technical report for a DOE project focused on the electrochemical conversion of CO 2 in non-aqueous solvents to novel products. Electrochemical reduction of CO 2 provides an attractive route to produce valuable fuels and chemicals that can simultaneously lower greenhouse gas emissions when powered by renewable electricity. While recent technological advances have shown the feasibility of industrial CO 2 electroreduction, many challenges remain to improve this technology and expand the list of economically viable products. The vast majority of electrochemical CO 2 reduction research has been conducted in aqueous media under neutral to alkaline conditions, leading to commonly reported products including carbon monoxide, formic acid, methane, methanol, ethylene, acetic acid, and ethanol. In comparison, non-aqueous media for CO 2 reduction has been underexplored but represents a possible avenue to yield new products and improved operating conditions. The aim of the project was to convert waste CO 2 in the form of flue gas to a multicarbon C2 - C4 chemical product in a reactor designed to achieve economically competitive values of current density and selectivity. The project strived to advance the technology readiness of an electrochemical CO 2 reduction process in alcohol solvents from the proof-of-concept stage to a device capable of meeting performance metrics for commercial viability. In the initial plan, the University of Louisville researchers were to focus on investigating the electrochemical process and improving the faradaic efficiency for novel C2 – C4 species, while also working on a parallel effort to build a practical electrolysis reactor to markedly increase the CO 2 reduction current density. The reactor development effort also aimed to engineer a dual-electrolyte feed strategy with non-aqueous catholyte and aqueous anolyte to promote water oxidation as the coupling anodic half-reaction to enable a sustainable and economical overall process. At the outset, the University of North Dakota was to investigate the feasibility of operating directly from coal-derived flue gas without separate capture and purification. The research team sought to determine impurity effects and test mitigation strategies, as well as engineer the gaseous feed system for high reactor tolerance to lower CO 2 concentration. In the last half year of the project, the focus was planned to shift to integrating the advances in the catalysis, electrochemical conditions, reactor design, and flue gas compatibility into a fully functional device and improve it for maximum current density and faradaic efficiency for C2 – C4 species. Knowledge of the full system components, constraints, and maximum performance was then to be used as the basis for a thorough technoeconomic analysis (TEA) and life cycle analysis (LCA) at the end of the project.

01 COAL, LIGNITE, AND PEAT↗

A Lunar Water ISRU System Study for Human-Scale Propellant Production

NASA’s plans to return to the moon in the Artemis campaign include intentions for sustainable surface operations. In-Situ Resource Utilization (ISRU) is key to enabling sustainability. The use of water, which has been identified in the lunar polar regions [1-4], could be pivotal to a sustainable architecture. While water has been identified, there is limited information regarding its form and distribution. The Lunar Water ISRU Measurement Study (LWIMS) [5] outlined the current state of knowledge regarding water resources on the moon, what information is lacking, and how to approach a measurement campaign to close those knowledge gaps. However the NASA timeline necessitates that lunar surface water prospecting, or reconnaissance, efforts and ISRU system design to occur in parallel. ISRU hardware and systems development must proceed without key resource knowledge. Yet, engineering design requires come constraints and targets to work to. The goal of this study was to define a set of lunar water reference cases, based on the cur-rent lunar water information, to help focus ISRU de-sign and planning. This approach is similar to an study focused on Mars resources called Mars Water ISRU Planning (MWIP) [6].

Lunar Water↗

Testing the Gravitational Redshift With an Inner Solar System Probe: the Veritas Case

The NASA Discovery-class mission VERITAS, selected in June 2021, will be launched towards Venus after 2027. In addition to the science instrumentation that will build global foundational geophysical datasets, VERITAS proposed to conduct a technology demonstration for the Deep Space Atomic Clock (DSAC-2). A first DSAC successfully operated in low-Earth orbit for more than two years, demonstrated the trapped ion atomic clock technology, and established a new level of performance for clocks in space. DSAC-2 would have further improvements in size, power, and performance. It would host a 1× 10 -13 grade USO to produce a frequency output with short-term stability of less than 2× 10 -13 /√ τ (where τ is the averaging time).However, due to funding shortfalls, DSAC-2, had to be canceled. The initially foreseen presence of an atomic clock on board the probe, however, raised the question whether this kind of instrumentation could be useful not only for navigation and time transfer but also for fundamental physics tests. In this work, we consider the DSAC-2 atomic clock and VERITAS mission as a specific example to measure possible discrepancies in the redshift predicted by General Relativity by using an atomic clock onboard an interplanetary spacecraft. In particular we investigate the possibility of measuring possible violations of the Local Lorentz Invariance and Local Position Invariance principles. We perform accurate simulations of the experiment during the VERITAS cruise phase. We consider different parametrizations of the possible violations of the General Relativity, different operational conditions, and several different assumptions on the expected measurement performance. We show that DSAC-2 onboard VERITAS would provide new and improved constraints with respect to the current knowledge. Our analysis shows the scientific value of atomic clocks like DSAC-2 hosted onboard interplanetary spacecraft.

Fabrizio De Marchi↗

Design enhancement tools in MSC/NASTRAN

Design sensitivity is the calculation of derivatives of constraint functions with respect to design variables. While a knowledge of these derivatives is useful in its own right, the derivatives are required in many efficient optimization methods. Constraint derivatives are also required in some reanalysis methods. It is shown where the sensitivity coefficients fit into the scheme of a basic organization of an optimization procedure. The analyzer is to be taken as MSC/NASTRAN. The terminator program monitors the termination criteria and ends the optimization procedure when the criteria are satisfied. This program can reside in several plances: in the optimizer itself, in a user written code, or as part of the MSC/EOS (Engineering Operating System) MSC/EOS currently under development. Since several excellent optimization codes exist and since they require such very specialized technical knowledge, the optimizer under the new MSC/EOS is considered to be selected and supplied by the user to meet his specific needs and preferences. The one exception to this is a fully stressed design (FSD) based on simple scaling. The gradients are currently supplied by various design sensitivity options now existing in MSC/NASTRAN's design sensitivity analysis (DSA).

Wallerstein, D. V.↗

A Systems Architecting Methodology Using Bloom’s Taxonomy to Promote Creative Engineering Synthesis

Architecting complex systems requires high-level cognitive processing and extensive knowledge of the system elements, functions, relationships, and constraints. This paper describes a systems architecting methodology implemented through cognitive psychological creative processes using Bloom’s taxonomy as a framework to generate the expert knowledge required to effectively and systematically synthesize new systems. Systems architecting activities were carried out to identify, develop, and capture factual and conceptual knowledge relevant to the system subject matter and functional elements, as well as to facilitate active processing of the knowledge through remembering, understanding, applying, analyzing, evaluating, and creating. Dual channel and limited capacity principles of learning were incorporated into the format of the systems engineering tools developed to assist with information processing and retention. Meta-cognitive strategies associated with memory and creative idea generation were implemented into the methodology to effectively and efficiently develop system alternatives using the full collection of knowledge. Synthesis of an orbiting sample capture and orientation system architecture to enable spacecraft-based on orbit capture of a Mars sample container for a potential Mars Sample Return campaign was used as a case study.

Adajian, Rama↗

On the utilization of engineering knowledge in design optimization

Some current research work conducted at the University of Michigan is described to illustrate efforts for incorporating knowledge in optimization in a nontraditional way. The incorporation of available knowledge in a logic structure is examined in two circumstances. The first examines the possibility of introducing global design information in a local active set strategy implemented during the iterations of projection-type algorithms for nonlinearly constrained problems. The technique used algorithms for nonlinearly constrained problems. The technique used combines global and local monotinicity analysis of the objective and constraint functions. The second examines a knowledge-based program which aids the user to create condigurations that are most desirable from the manufacturing assembly viewpoint. The data bank used is the classification scheme suggested by Boothroyd. The important aspect of this program is that it is an aid for synthesis intended for use in the design concept phase in a way similar to the so-called idea-triggers in creativity-enhancement techniques like brain-storming. The idea generation, however, is not random but it is driven by the goal of achieving the best acceptable configuration.

Papalambros, P.↗