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At least 1,477 records · Page 82

The Psyche Planning Software Subsystem: Creating a Robust Toolset for a Discovery-class Mission

Psyche is a Discovery-class mission to the small metal-rich asteroid (16) Psyche, and is slated to launch in 2022. Psyche, like many missions, requires low-cost activity planning and sequence generation that serves as the backbone to overall uplink design. Such tools must be maintainable over long periods of operations, and powerful enough to solve complex issues that deep-space one-off missions encounter. In this paper we introduce cost-effective solutions that leverage inner- and open-source principles to meet a variety of common and novel use cases.The uplink process that was designed to meet these challenges is presented, as well as the data-flow through the high-level architecture of the planning software subsystem. The user-facing planning tools are described, particularly the Science Opportunity Analyzer, the Plan Editor, Psyche’s planning automation in the Blackbird framework, and Psyche Simulation Reports. All these applications are either new or have been substantially revamped to meet Psyche’s concept of operations. In particular, ensuring the entire toolchain can correctly process epoch-relative activities is discussed. Underlying the main applications are a common set of dependencies developed and maintained by a new cross-mission association of planning developers. In this way, Psyche can inherit well-tested functionality which saves effort and ensures its developers can focus on solving domain challenges. Quality control of the applications and libraries is ensured with a code-review and unit-test based novel ‘CM lite’ process. Collaboration with international industry and academia using the open-source modules is already occurring.The planning and scheduling software is designed to maximize operator awareness of the integrated plan at every step of the process and use common interfaces and file formats to easily transfer information. Design choices plus the team’s test-driven development process enables more expansive capabilities compared to the decentralized planning and sequence generation functions typical of Discovery-class orbiters without significant development cost increases. Benefits and drawbacks of Psyche’s approach are discussed, including comparison to other missions and tools where appropriate.

Ramanathan, Keshav↗

The GRO remote terminal system

In March 1992, NASA HQ challenged GSFC/Code 531 to propose a fast, low-cost approach to close the Tracking Data Relay Satellite System (TDRSS) Zone-of-Exclusion (ZOE) over the Indian Ocean in order to provide global communications coverage for the Compton Gamma Ray Observatory (GRO) spacecraft. GRO had lost its tape recording capability which limited its valuable science data return to real-time contacts with the TDRS-E and TDRS-W synchronous data relay satellites, yielding only approximately 62 percent of the possible data obtainable. To achieve global coverage, a TDRS spacecraft would have to be moved over the Indian Ocean out of line-of-sight control of White Sands Ground Terminal (WSGT). To minimize operations life cycle costs, Headquarters also set a goal for remote control, from the WSGT, of the overseas ground station which was required for direct communications with TDRS-1. On August 27, 1992, Code 531 was given the go ahead to implement the proposed GRO Relay Terminal System (GRTS). This paper describes the Remote Ground Relay Terminal (RGRT) which went operational at the Canberra Deep Space Communications Complex (CDSCC) in Canberra, Australia in December 1993 and is currently augmenting the TDRSS constellation in returning between 80-100 percent of GRO science data under the control of a single operator at WSGT.

Zillig, David J.↗

A biophysical approach to the performance diagnosis of human–building energy interaction: Information (bits) modeling, algorithm, and indicators of energy flow complexity

Highlights: • Energy-related human-building interaction is analyzed at the biophysical system scale. • Energy flow-networking model of environmental building performance is suggested. • Information is suggested as a holistic building performance metric. - Abstract: This article addresses the utmost upstream impacts of human–building energy interaction by proposing a network-based model, algorithms, and indicators. Hypothesizing that human behavior is a key factor for the symbiotic development of building and the geobiosphere system, the author seeks emergy (spelled with an “m”)-information integrated measures to indicate dynamic system-level performance of the interaction with building energy flow topology. To validate the hypothesis and methods, four representative building cases were tested on the building form (envelope) of (i) a building with no occupant intervention (baseline), (ii) a building controlled by responsive human behavior (bioclimatic adaptation), (iii) a building with reinforced insulation and behavior-dominated control (passive design), and (iv) a net-zero energy building (NZEB). The results demonstrate that adaptive human behavior in building operation increases the information content and complexity of energy-flow networking, improving performance and sustainability. Findings also reveal that increasing information, complexity, and power (energy availability over time) parallel the general energetic features of developing biophysical systems (greater feedback, internalization, and recycling of materials and energies). It becames clear that active behavioral response is a dominant agent of sustainable environments even on a far broader system scale.

54 ENVIRONMENTAL SCIENCES↗

Service Life Extension of the Propulsion System of Long-Term Manned Orbital Stations

One of the critical non-replaceable systems of a long-term manned orbital station is the propulsion system. Since the propulsion system operates beginning with the launch of station elements into orbit, its service life determines the service life of the station overall. Weighing almost a million pounds, the International Space Station (ISS) is about four times as large as the Russian space station Mir and about five times as large as the U.S. Skylab. Constructed over a span of more than a decade with the help of over 100 space flights, elements and modules of the ISS provide more research space than any spacecraft ever built. Originally envisaged for a service life of fifteen years, this Earth orbiting laboratory has been in orbit since 1998. Some elements that have been launched later in the assembly sequence were not yet built when the first elements were placed in orbit. Hence, some of the early modules that were launched at the inception of the program were already nearing the end of their design life when the ISS was finally ready and operational. To maximize the return on global investments on ISS, it is essential for the valuable research on ISS to continue as long as the station can be sustained safely in orbit. This paper describes the work performed to extend the service life of the ISS propulsion system. A system comprises of many components with varying failure rates. Reliability of a system is the probability that it will perform its intended function under encountered operating conditions, for a specified period of time. As we are interested in finding out how reliable a system would be in the future, reliability expressed as a function of time provides valuable insight. In a hypothetical bathtub shaped failure rate curve, the failure rate, defined as the number of failures per unit time that a currently healthy component will suffer in a given future time interval, decreases during infant-mortality period, stays nearly constant during the service life and increases at the end when the design service life ends and wear-out phase begins. However, the component failure rates do not remain constant over the entire cycle life. The failure rate depends on various factors such as design complexity, current age of the component, operating conditions, severity of environmental stress factors, etc. Development, qualification and acceptance test processes provide rigorous screening of components to weed out imperfections that might otherwise cause infant mortality failures. If sufficient samples are tested to failure, the failure time versus failure quantity can be analyzed statistically to develop a failure probability distribution function (PDF), a statistical model of the probability of failure versus time. Driven by cost and schedule constraints however, spacecraft components are generally not tested in large numbers. Uncertainties in failure rate and remaining life estimates increase when fewer units are tested. To account for this, spacecraft operators prefer to limit useful operations to a period shorter than the maximum demonstrated service life of the weakest component. Running each component to its failure to determine the maximum possible service life of a system can become overly expensive and impractical. Spacecraft operators therefore, specify the required service life and an acceptable factor of safety (FOS). The designers use these requirements to limit the life test duration. Midway through the design life, when benefits justify additional investments, supplementary life test may be performed to demonstrate the capability to safely extend the service life of the system. An innovative approach is required to evaluate the entire system, without having to go through an elaborate test program of propulsion system elements. Evaluating every component through a brute force test program would be a cost prohibitive and time consuming endeavor. ISS propulsion system components were designed and built decades ago. There are no representative ground test articles for some of the components. A 'test everything' approach would require manufacturing new test articles. The paper outlines some of the techniques used for selective testing, by way of cherry picking candidate components based on failure mode effects analysis, system level impacts, hazard analysis, etc. The type of testing required for extending the service life depends on the design and criticality of the component, failure modes and failure mechanisms, life cycle margin provided by the original certification, operational and environmental stresses encountered, etc. When specific failure mechanism being considered and the underlying relationship of that mode to the stresses provided in the test can be correlated by supporting analysis, time and effort required for conducting life extension testing can be significantly reduced. Exposure to corrosive propellants over long periods of time, for instance, lead to specific failure mechanisms in several components used in the propulsion system. Using Arrhenius model, which is tied to chemically dependent failure mechanisms such as corrosion or chemical reactions, it is possible to subject carefully selected test articles to accelerated life test. Arrhenius model reflects the proportional relationship between time to failure of a component and the exponential of the inverse of absolute temperature acting on the component. The acceleration factor is used to perform tests at higher stresses that allow direct correlation between the times to failure at a high test temperature to the temperatures to be expected in actual use. As long as the temperatures are such that new failure mechanisms are not introduced, this becomes a very useful method for testing to failure a relatively small sample of items for a much shorter amount of time. In this article, based on the example of the propulsion system of the first ISS module Zarya, theoretical approaches and practical activities of extending the service life of the propulsion system are reviewed with the goal of determining the maximum duration of its safe operation.

Kamath, Ulhas↗

On the influence of over-parameterization in manifold based surrogates and deep neural operators

Constructing accurate and generalizable approximators (surrogate models) for complex physico-chemical processes exhibiting highly non-smooth dynamics is challenging. The main question is what type of surrogate models we should construct and should these models be under-parameterized or over-parameterized. In this work, we propose new developments and perform comparisons for two promising approaches: manifold-based polynomial chaos expansion (m-PCE) and the deep neural operator (DeepONet), and we examine the effect of over-parameterization on generalization. While m-PCE enables the construction of a mapping by first identifying low-dimensional embeddings of the input functions, parameters, and quantities of interest (QoIs), a neural operator learns the nonlinear mapping via the use of deep neural networks. Here, we demonstrate the performance of these methods in terms of generalization accuracy by solving the 2D time-dependent Brusselator reaction-diffusion system with uncertainty sources, modeling an autocatalytic chemical reaction between two species. We first propose an extension of the m-PCE by constructing a mapping between latent spaces formed by two separate embeddings of the input functions and the output QoIs. To further enhance the accuracy of the DeepONet, we introduce weight self-adaptivity in the loss function. We demonstrate that the performance of m-PCE and DeepONet is comparable for cases of relatively smooth input-output mappings. However, when highly non-smooth dynamics is considered, DeepONet shows higher approximation accuracy. We also find that for m-PCE, modest over-parameterization leads to better generalization, both within and outside of distribution, whereas aggressive over-parameterization leads to over-fitting. In contrast, an even highly over-parameterized DeepONet leads to better generalization for both smooth and non-smooth dynamics. Furthermore, we compare the performance of the above models with another recently proposed operator learning model, the Fourier Neural Operator, and show that its over-parameterization also leads to better generalization. Taken together, our studies show that m-PCE can provide very good accuracy at very low training cost, whereas a highly over-parameterized DeepONet can provide better accuracy and robustness to noise but at higher training cost. In both methods, the inference cost is negligible.

97 MATHEMATICS AND COMPUTING↗

Layered virus protection for the operations and administrative messaging system

NASA's Deep Space Network (DSN) is critical in supporting the wide variety of operating and plannedunmanned flight projects. For day-to-day operations it relies on email communication between the three Deep Space Communication Complexes (Canberra, Goldstone, Madrid) and NASA's Jet Propulsion Laboratory. The Operations & Administrative Messaging system, based on the Microsoft Windows NTand Exchange platform, provides the infrastructure that is required for reliable, mission-critical messaging. The reliability of this system, however, is threatened by the proliferation of email viruses that continue to spread at alarming rates. A layered approach to email security has been implemented across the DSN to protect against this threat.

IT security antivirus↗

Computational aspects of the prediction of multidimensional transonic flows in turbomachinery

The analytical prediction and description of transonic flow in turbomachinery is complicated by three fundamental effects: (1) the fluid equations describing the transonic regime are inherently nonlinear, (2) shock waves may be present in the flow, and (3) turbomachine blading is geometrically complex, possessing large amounts of curvature, stagger, and twist. A three-dimensional computation procedure for the study of transonic turbomachine fluid mechanics is described. The fluid differential equations and corresponding difference operators are presented, the boundary conditions for complex blade shapes are described, and the computational implementation and mapping procedures are developed. Illustrative results of a typical unthrottled transonic rotor are also presented.

Oliver, D. A.↗

Chapter Nine - Automated Optimal Control in Energy Systems: The Reinforcement Learning Approach

With the development of smart grid technologies an increasing number of new devices and participants have joined modern energy systems and are inevitably making them more complicated and interdependent than ever. Optimally controlling such a complex energy system and maintaining its operation in a high-efficient, secure, and resilient manner are challenging tasks to the system operators. Fortunately, the revolution in deep learning and artificial intelligence (AI), both from hardware and algorithms perspectives, has provided new ideas and solutions to many previously intractable problems. As a result, this advance in computer science also sparked great research interests in utilizing AI in solving engineering problems related to the modern energy systems. Among many AI techniques, deep reinforcement learning (DRL) has demonstrated great potential for solving sequential optimization problems, which are very common in the engineering domains. Its ability to handle nonlinearity and stochasticity in controlled systems has out-competed many traditional optimal control algorithms. Therefore in this chapter, we focus on the state-of-the-art of DRL concepts and related algorithms, compare their pros and cons with traditional optimal control approaches and discuss the typical workflow for leveraging RL in solving complex problems in modern energy systems.

artificial intelligence↗

Measurement-based Power System Dynamic Model Reductions

Interconnected power systems have experienced a significant increase in its size and complexity, due to continuous topology and operating condition changes. It is computationally burdensome to represent entire system models in detail to conduct power system analysis. In the large-scale power system dynamic simulations, the models in the study area need to be retained in detail while the external area can be reduced without compromising accuracy, which can preserve the full system dynamic properties. This paper develops measurement-based continuous-time dynamic equivalents for power systems in order to both improve the model accuracy of the reduced system and increase the speed of dynamic simulation. Our method is based on a set of measurements at the boundary nodes between the study area and external area. Case studies demonstrate that the measurement-based model can capture the full system behavior sufficiently, and improve computational efficiency.Interconnected power systems experienced a significant increase in size and complexity. It is computationally burdensome to represent the entire system in detail to conduct power system analysis. Therefore, the model of the study system must be retained in detail while the external system can be reduced using system reduction techniques. This paper proposes a measurement-based dynamic equivalent in order to increase both model accuracy and simulation speed. The proposed method uses a set of measurements at the boundary nodes between the study area and external area for model parameter identification. Case studies demonstrate that the measurement-based technique can capture the main system behaviors accurately and improve computational efficiency.

Xue, Yaosuo↗

Operability engineering in the Deep Space Network

Many operability problems exist at the three Deep Space Communications Complexes (DSCC's) of the Deep Space Network (DSN). Four years ago, the position of DSN Operability Engineer was created to provide the opportunity for someone to take a system-level approach to solving these problems. Since that time, a process has been developed for personnel and development engineers and for enforcing user interface standards in software designed for the DSCC's. Plans are for the participation of operations personnel in the product life-cycle to expand in the future.

Wilkinson, Belinda↗

The multi-faceted inverted harmonic oscillator: Chaos and complexity

The harmonic oscillator is the paragon of physical models; conceptually and computationally simple, yet rich enough to teach us about physics on scales that span classical mechanics to quantum field theory. This multifaceted nature extends also to its inverted counterpart, in which the oscillator frequency is analytically continued to pure imaginary values. In this article we probe the inverted harmonic oscillator (IHO) with recently developed quantum chaos diagnostics such as the out-of-time-order correlator (OTOC) and the circuit complexity. In particular, we study the OTOC for the displacement operator of the IHO with and without a non-Gaussian cubic perturbation to explore genuine and quasi scrambling respectively. In addition, we compute the full quantum Lyapunov spectrum for the inverted oscillator, finding a paired structure among the Lyapunov exponents. We also use the Heisenberg group to compute the complexity for the time evolved displacement operator, which displays chaotic behaviour. Finally, we extended our analysis to N-inverted harmonic oscillators to study the behaviour of complexity at the different timescales encoded in dissipation, scrambling and asymptotic regimes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Recursive dynamics for flexible multibody systems using spatial operators

Due to their structural flexibility, spacecraft and space manipulators are multibody systems with complex dynamics and possess a large number of degrees of freedom. Here the spatial operator algebra methodology is used to develop a new dynamics formulation and spatially recursive algorithms for such flexible multibody systems. A key feature of the formulation is that the operator description of the flexible system dynamics is identical in form to the corresponding operator description of the dynamics of rigid multibody systems. A significant advantage of this unifying approach is that it allows ideas and techniques for rigid multibody systems to be easily applied to flexible multibody systems. The algorithms use standard finite-element and assumed modes models for the individual body deformation. A Newton-Euler Operator Factorization of the mass matrix of the multibody system is first developed. It forms the basis for recursive algorithms such as for the inverse dynamics, the computation of the mass matrix, and the composite body forward dynamics for the system. Subsequently, an alternative Innovations Operator Factorization of the mass matrix, each of whose factors is invertible, is developed. It leads to an operator expression for the inverse of the mass matrix, and forms the basis for the recursive articulated body forward dynamics algorithm for the flexible multibody system. For simplicity, most of the development here focuses on serial chain multibody systems. However, extensions of the algorithms to general topology flexible multibody systems are described. While the computational cost of the algorithms depends on factors such as the topology and the amount of flexibility in the multibody system, in general, it appears that in contrast to the rigid multibody case, the articulated body forward dynamics algorithm is the more efficient algorithm for flexible multibody systems containing even a small number of flexible bodies. The variety of algorithms described here permits a user to choose the algorithm which is optimal for the multibody system at hand. The availability of a number of algorithms is even more important for real-time applications, where implementation on parallel processors or custom computing hardware is often necessary to maximize speed.

Jain, A.↗

Approximate quantum circuit synthesis using block encodings

One of the challenges in quantum computing is the synthesis of unitary operators into quantum circuits with polylogarithmic gate complexity. Exact synthesis of generic unitaries requires an exponential number of gates in general. We propose a novel approximate quantum circuit synthesis technique by relaxing the unitary constraints and interchanging them for ancilla qubits via block encodings. This approach combines smaller block encodings, which are easier to synthesize, into quantum circuits for larger operators. Due to the use of block encodings, our technique is not limited to unitary operators and can be applied for the synthesis of arbitrary operators. Overall, we show that operators which can be approximated by a canonical polyadic expression with a polylogarithmic number of terms can be synthesized with polylogarithmic gate complexity with respect to the matrix dimension.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Resilience Modeling in Complex Engineered Systems with Human-Machine Interactions

In recent times, there has been a growing interest in resilience-based design. Resilience-based design operates on the concept that failures and unexpected events will happen, and when they occur, complex engineered systems should be able to operate within acceptable bounds and recover reasonably. Humans can contribute to the resilience of a system by quickly detecting unforeseen events and taking corrective measures. To this effect, researchers have proposed guidelines and design approaches that can help promote human-system resilience. However, there is no early design stage tool to validate if a system is indeed resilient after applying these guidelines and design methods. In this research, we integrate the Human Error and Functional Failure Reasoning (HEFFR) framework into the fmdtools toolkit to enable designers to model the combined (machine, human, and joint) failures, including their propagation and dynamic effects, during early design stages. This integrated tool also allows designers to model the effects of performance shaping factors, team dynamics, and human-machine interactions in systems of systems. A demonstrative example of a remotely operated rover is explored to demonstrate how this approach can be applied to understand resilience in complex engineered systems with human interactions.

Lukman Irshad↗

Validation of Microgrid Algorithms using At-Scale Simulation and Emulation Real-Time (ASSERT) Framework

This paper presents a novel framework for validating microgrid algorithms using a At-Scale Simulation and Emulation Real-Time (ASSERT) setup. The proposed universal microgrid validation framework was developed at Advanced Research on Integrated Energy Systems (ARIES) platform at National Renewable Energy Laboratory in Golden, Colorado, USA. As the number of intercommunicating devices grow in power systems, along with more integration of inverter-based resources, developing and validating microgrid operation and control algorithms is becoming complex and time-consuming task. The ASSERT framework is designed to provide a comprehensive, accurate, and efficient solution for validating microgrid algorithms. It combines at-scale simulations and real-time emulation to create a virtual environment that closely mimics the behavior of a physical microgrids which are characteristic of future smart distribution systems, such as: (1) increasing variability in the physical size of new energy technologies; (2) requiring control large numbers of interconnected devices, and (3) integrating diverse technologies that have not previously worked together. The paper describes the technical details of the ASSERT framework, its validation process, and its benefits over traditional validation methods. The framework's ability to validate the performance of various algorithms is demonstrated through a case study, which shows that it can effectively identify the weaknesses of an algorithm and optimize its performance. Overall, the ASSERT framework is a promising tool that can significantly enhance the development and validation of microgrid algorithms, leading to more reliable and efficient microgrid operation.

at-scale↗

The Role of Tactile Cueing in Multimodal Displays: Application in Complex Task Environments for Space Exploration

This paper reviews the potential role of tactile cueing in multimodal displays that will need to be developed to enable Extravehicular Activity (EVA) and telerobotic exploration for both Moon and Mars missions. Specific missions to be accomplished during EVA surface and space operations will include construction and assembly, surface and geologic exploration, and excavation for protective shelter from radiation and significant solar particle events. EVA crews have limited time, resources, mobility, visibility and dexterity, and activities take place in a perceptually impoverished environment. Limitations include a loss of spatial reference frame leading to spatial disorientation, a loss of information normally provided by the auditory and somato-sensory systems, and a restricted field of view. In addition, in non-surface operations crew must be able to navigate and operate in a fully six degree of freedom (DoF) environment. Fatigue resulting from suited EVA tasks will also critically affect performance and safety. In order to mitigate these factors, it is important that crews be well trained prior to executing actual missions. There is a critical need to design multimodal interfaces to optimize performance under these conditions for both training and actual operations. Because of the high level of complexity of the EVA environment on the one hand, and the “sensory restrictions” on the other, it makes sense to envisage enhancements to the astronauts’ visual environment. Such an enriched environment may include virtual auditory as well as tactile inputs.

multimodal displays↗

Unleashing the Power of Anaerobic–Phototrophic Membrane Bioreactors for Sustainable Bioregenerative Life Support

Using the core ideals of bioregenerative life support, an anaerobic–phototrophic membrane bioreactor (APMBR) has been designed and operated at NASA’s Kennedy Space Center to treat complex wastewaters with the goal of closing water and nutrient cycles on early planetary bases. The system combined the previously operated anaerobic membrane bioreactor and the phototrophic membrane bioreactor that have been detailed in presentations at previous ICES conferences. This newly combined system is able to treat complex wastewater with completely automated controls on a small footprint. The treatment of wastes in this APMBR is as follows: (1) the waste enters the anaerobic subsystem, where solids are hydrolyzed and carbon is removed via anaerobic digestion, (2) an ultrafiltration membrane is used to separate the solids and the recovered water, (3) the effluent from the anaerobic subsystem is fed to the phototrophic subsystem on command, (4) the algae–bacteria consortium aids in nitrification in the recovered water, and (5) an ultrafiltration membrane is used to separate the algae and the final recovered water. The recovered water from the APMBR is rich in nutrients, making it a sustainable source of fertilizer for downstream hydroponic systems. This conference paper will detail the design and operation of the APMBR as a bioregenerative alternative to physical–chemical systems or bag and storage systems. In this paper, data will be presented on subsystem water quality, membrane performance, and effluent quality. Overall, the APMBR has the ability to treat wastewater using a combination of biological and filtration technologies that allow for higher removal efficiencies, low consumable use, and small footprint.

Jason A. Fischer↗

Short-term solar irradiance forecasting via satellite/model coupling

Here, the short-term (0-3 h) prediction of solar insolation for renewable energy production is a problem well-suited to satellite-based techniques. The spatial, spectral, temporal and radiometric resolution of instrumentation hosted on the geostationary platform allows these satellites to describe the current cloud spatial distribution and optical properties. These properties relate directly to the transient properties of the downwelling solar irradiance at the surface, which come in the form of 'ramps' that pose a central challenge to energy load balancing in a spatially distributed network of solar farms. The short-term evolution of the cloud field may be approximated to first order simply as translational, but care must be taken in how the advection is handled and where the impacts are assigned. In this research, we describe how geostationary satellite observations are used with operational cloud masking and retrieval algorithms, wind field data from Numerical Weather Prediction (NWP), and radiative transfer calculations to produce short-term forecasts of solar insolation for applications in solar power generation. The scheme utilizes retrieved cloud properties to group pixels into contiguous cloud objects whose future positions are predicted using four-dimensional (space + time) model wind fields, selecting steering levels corresponding to the cloud height properties of each cloud group. The shadows associated with these clouds are adjusted for sensor viewing parallax displacement and combined with solar geometry and terrain height to determine the actual location of cloud shadows. For mid/high-level clouds at mid-latitudes and high solar zenith angles, the combined displacements from these geometric considerations are non-negligible. The cloud information is used to initialize a radiative transfer model that computes the direct and diffuse-sky solar insolation at both shadow locations and intervening clear-sky regions. Here, we describe the formulation of the algorithm and validate its performance against Surface Radiation (SURFRAD; Augustine et al., 2000, 2005) network observations. Typical errors range from 8.5% to 17.2% depending on the complexity of cloud regimes, and an operational demonstration outperformed persistence-based forecasting of Global Horizontal Irradiance (GHI) under all conditions by ~10 W/m 2 .

14 SOLAR ENERGY↗