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This presentation is from the ReEDS webinar given on October 31, 2019. This presentation provides an overview of the Regional Energy Deployment System (ReEDS) model.
The Regional Energy Deployment System (ReEDS) model is NREL’s flagship capacity expansion planning tool for the electric power sector in North America. Originally developed in 2001, ReEDS has been used primarily by NREL researchers to conduct high-profile analysis of the potential evolution of the electricity system. In September 2019, the core model is being released for public use. In this presentation, we provide an overview of the model architecture, key model features, data sources, lessons learned, and sample model results for the North American electric power system.
Dry cooling, where forced air is the heat transfer medium, is a preferable cooling method in arid locations lacking readily available process water. However, such locations often experience high ambient temperatures that limit the effectiveness of air cooling. The objective of this study is to quantify the economic and energetic benefits of heat transfer intensification via the implementation of aeroelastically fluttering reeds to the air-cooled condenser of a methanol distillation column. Condenser size and performance, regarding recovered methanol and required fan power, is evaluated across condenser operating temperatures (T cond ) from 60 to 62°C and heat transfer coefficients (U) U base –2U base for a range of inlet air temperatures based on ambient temperature data from Yuma, Arizona. Under typical design sizing, condenser capital cost was reduced by 6%–35% (1.3U base –2U base ) and nominal methanol recovery was increased from 0.26% to 0.38% (T cond = 62–60°C). At optimized condenser size, all enhanced U and T cond pairs increase methanol recovery and reduce fan power costs compared to the optimal Ubase reference. Overall, using enhanced heat transfer to maintain condenser temperature under a wider range of inlet conditions, rather than to reduce operation temperature, produces more favorable performance. Methanol price is not a determining factor in which pairs are profitable. Analysis was repeated for a global warming scenario, revealing more valuable improvements under elevated temperatures. Energy savings from condenser improvement to a methanol production system are not significant with respect to an optimized conventional system. Furthermore, unit economic and energetic incentives suggest implementation of fluttering reeds may be justified in other applications.
WBM-hydro-ReEDS is a tool for converting daily reservoir release simulations from the WMB model to monthly, plant-level hydropower generation estimates, and then to inputs for the ReEDS model.
This report explores how error-correction data generated by a Reed-Solomon code may be used as a mechanism to apply changes to an existing installed codebase. Using the Reed-Solomon code to generate error-correction data for a changed or updated codebase will allow the error-correction data to be applied to an existing codebase to both validate and introduce changes or updates from some upstream source to the existing installed codebase.
As the volume of data acquired by space-based sensors increases, mission data compression/decompression and forward error correction code processing performance must likewise scale. This competency development effort was explored using the General Purpose Graphics Processing Unit (GPGPU) to accomplish high-rate Rice Decompression and high-rate Reed-Solomon (RS) decoding at the satellite mission ground station. Each algorithm was implemented and benchmarked on a single GPGPU. Distributed processing across one to four GPGPUs was also investigated. The results show that the GPGPU has considerable potential for performing satellite communication Data Signal Processing, with three times or better performance improvements and up to ten times reduction in cost over custom hardware, at least in the case of Rice Decompression and Reed-Solomon Decoding.
The synchronization capabilities of Reed-Solomon codes when an appropriate coset of the code is used instead of the code itself are examined. In this case an E-error correcting Reed-Solomon code is transformed into a code capable of determining that there are m symbols out of sync, if e symbol errors occurred, whenever m + e E. In the event that m = 0, i.e., the word is in sync, then decoder will correct any pattern of E - 1 on fewer symbol errors.
The need for efficient space communication at very low bit error probabilities to the specification and implementation of a concatenated coding system using an interleaved Reed-Solomon code as the outer code and a Viterbi-decoded convolutional code as the inner code. Experimental results of this channel coding system are presented under an emulated S-band uplink and X-band downlink two-way space communication channel, where both uplink and downlink have strong carrier power. This work was performed under the NASA End-to-End Data Systems program at JPL. Test results verify that at a bit error probability of 10 to the -6 power or less, this concatenated coding system does provide a coding gain of 2.5 dB or more over the Viterbi-decoded convolutional-only coding system. These tests also show that a desirable interleaving depth for the Reed-Solomon outer code is 8 or more. The impact of this "virtually" error-free space communication link on the transmission of images is discussed and examples of simulation results are given.
Modular Reed-solomon encoder uses identical custom VLSI chips called "symbol slices." By cascading and properly interconnecting group of these chips, encoder is made for any desired error-correcting capability and interleaving level. VLSI encoder requires only one-tenth the number of chips required by conventional Reed-Solomon Circuit implemented with discrete IC's.
The REEDS (rocket exhaust effluent diffusion single layer) computer code is used for the estimation of certain rocket exhaust effluent concentrations and dosages and their distributions near the Earth's surface following a rocket launch event. Output from REEDS is used in producing near real time air quality and environmental assessments of the effects of certain potentially harmful effluents, namely HCl, Al2O3, CO, and NO.
An algorithm which enables one Reed-Solomon decoder to process other Reed-Solomon encoded data from a different code is presented. The sole requirement is that both codes have the same length, the same rate, and the same field of coefficients. It is pointed out that only very simple pre- and post-processing hardware is needed to resolve an encoder/decoder incompatibility and that no encoder modification is needed.
Consider an (n,k) linear code with symbols from GF(2 sup M). If each code symbol is represented by a m-tuple over GF(2) using certain basis for GF(2 sup M), a binary (nm,km) linear code is obtained. The weight distribution of a binary linear code obtained in this manner is investigated. Weight enumerators for binary linear codes obtained from Reed-Solomon codes over GF(2 sup M) generated by polynomials, (X-alpha), (X-l)(X-alpha), (X-alpha)(X-alpha squared) and (X-l)(X-alpha)(X-alpha squared) and their extended codes are presented, where alpha is a primitive element of GF(2 sup M). Binary codes derived from Reed-Solomon codes are often used for correcting multiple bursts of errors.
A concatenated coding consisting of an inner (7, 1/2) convolutional code and an outer (255, 223) Reed-Solomon code was recommended by the Consultative Committee for Space Data Systems for cross-supported space missions. The Reed-Solomon code that was chosen makes use of the Berlekamp encoding algorithm. Some peculiarities of this code that could give rise to synchronization problems are examined. Suggestions are given to alleviate these problems.
The very large-scale integration (VLSI) architecture of a single chip (255, 223) Reed-Solomon decoder for decoding both errors and erasures is described. A decoding failure detection capability is also included in this system so that the decoder will recognize a failure to decode instead of introducing additional errors. This could happen whenever the received word contains too many errors and erasures for the code to correct. The number of transistors needed to implement this decoder is estimated at about 75,000 if the delay for received message is not included. This is in contrast to the older transform decoding algorithm which needs about 100,000 transistors. However, the transform decoder is simpler in architecture than the time decoder. It is therefore possible to implement a single chip (255, 223) Reed-Solomon decoder with today's VLSI technology. An implementation strategy for the decoder system is presented. This represents the first step in a plan to take advantage of advanced coding techniques to realize a 2.0 dB coding gain for future space missions.
Presented as an implementation of a Reed-Solomon encode and decoder, which is 16-symbol error correcting, each symbol is 8 bits. This Reed-Solomon (RS) code is an efficient error correcting code that the National Aeronautics and Space Administration (NASA) will use in future space communications missions. A Very Large Scale Integration (VLSI) implementation of the encoder and decoder accepts data rates up 80 Mbps. A total of seven chips are needed for the decoder (four of the seven decoding chips are customized using 3-micron Complementary Metal Oxide Semiconduction (CMOS) technology) and one chip is required for the encoder. The decoder operates with the symbol clock being the system clock for the chip set. Approximately 1.65 billion Galois Field (GF) operations per second are achieved with the decoder chip set and 640 MOPS are achieved with the encoder chip.
Reed-Solomon (RS) codes form an important part of the high-rate downlink telemetry system for the Magellan mission, and the RS decoding function for this project will be done by DSN. Although the basic idea behind all Reed-Solomon decoding algorithms was developed by Berlekamp in 1968, there are dozens of variants of Berlekamp's algorithm in current use. An attempt to restore order is made by presenting a mathematical theory which explains the working of almost all known RS decoding algorithms. The key innovation that makes this possible is the unified approach to the solution of the key equation, which simultaneously describes the Berlekamp, Berlekamp-Massey, Euclid, and continued fractions approaches. Additionally, a detailed analysis is made of what can happen to a generic RS decoding algorithm when the number of errors and erasures exceeds the code's designed correction capability, and it is shown that while most published algorithms do not detect as many of these error-erasure patterns as possible, by making a small change in the algorithms, this problem can be overcome.
This tutorial attempts to provide a frank, step-by-step approach to Reed-Solomon (RS) error correction coding. RS encoding and RS decoding both with and without erasing code symbols are emphasized. There is no need to present rigorous proofs and extreme mathematical detail. Rather, the simple concepts of groups and fields, specifically Galois fields, are presented with a minimum of complexity. Before RS codes are presented, other block codes are presented as a technical introduction into coding. A primitive (15, 9) RS coding example is then completely developed from start to finish, demonstrating the encoding and decoding calculations and a derivation of the famous error-locator polynomial. The objective is to present practical information about Reed-Solomon coding in a manner such that it can be easily understood.
Control circuit designed for use with commercially available AHA4610 Reed-Solomon encoder. Needed to select depth of interleaving and to synchronize input and output blocks of data and parity bits with suitable clock signals. Circuit provides synchronizing and control signals for Reed-Solomon encoder. Encoder can operate with asynchronous input and output data streams at rates up to 80 Mb/s. Interleaving depth selectable, and accommodation to input data rate automatic.